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Abstract

Background: Diarrhea is a major cause of mortality and morbidity in under-5 children globally, especially in developing countries like Ethiopia. Limited research has used machine learning to predict childhood diarrhea. This study aimed to compare the predictive performance of ML algorithms for diarrhea in under-5 children in Ethiopia. Methods: The study utilized a dataset of 9501 under-5 children from the Ethiopia Demographic and Health Survey 2016. Five ML algorithms were used to build and compare predictive models. The model performance was evaluated using various metrics in Python. Boruta feature selection was employed, and data balancing techniques such as under-sampling, over-sampling, adaptive synthetic sampling, and synthetic minority oversampling as well as hyper parameter tuning methods were explored. Association rule mining was conducted using the Apriori algorithm in R to determine relationships between independent and target variables. Results: 10.2% of children had diarrhea. The Random Forest model had the best performance with 93.2% accuracy, 98.4% sensitivity, 85.5% specificity, and 0.916 AUC. The top predictors were residence, wealth index, and child age, number of living children, deworming, wasting, mother’s occupation, and education. Association rule mining identified the top 7 rules most associated with under-5 diarrhea in Ethiopia. Conclusion: The RF achieved the highest performance for predicting childhood diarrhea. Policymakers and healthcare providers can use these findings to develop targeted interventions to reduce diarrhea. Customizing strategies based on the identified association rules has the potential to improve child health and decrease the impact of diarrhea in Ethiopia.

Keywords

diarrhea Ethiopia machine learning predictors under-five children

Introduction

The World Health Organization defines diarrhea as it is the passing of at least three loose stools per 24 h and it primarily occurs after a bacterial, viral, or parasitic infection of the intestinal tract.¹ The commonest reported causative agents were Vibrio cholera, Escherichia coli, Salmonellae species, rotavirus, Giardia, Entamoeba, and the helminthes.² Diarrhea is a significant public health issue, being recognized as the second leading cause of mortality and morbidity among children under the age of five, especially in developing countries like Ethiopia.^3,4 Globally, there are nearly 1.7 billion cases of childhood diarrheal disease and it kills around 443 832 children under 5 years every year,^1,5 with Sub-Saharan Africa reporting the highest numbers.^4,6 Based on the 2016 Ethiopian Demographic and Health Survey (EDHS) report, the prevalence of diarrhea among children under the age of five in Ethiopia was 12%.⁷

According to the Integrated Global Plan of Action for the Prevention and Control of Pneumonia and Diarrhea, there is a standpoint to end up mortality caused by pneumonia and diarrhea by 2025 and it is encompassed both vital services and interventions to make a healthy environment, access to recognized and appropriate prevention and treatment measures, therefore the approach has aimed to decrease diarrhea mortality in children under-5 to <1 per 1000 live births.⁸ Patients with diarrhea will have a numerous problems such as loss of appetite, weight loss, and growth failure. Moreover, diarrhea causes water and electrolyte deficits and dehydration is the fatal complication.⁹ As a result diarrhea requires exigent and timely management to lessen its complications, it is better to recognize the predictors for crucial treatment and better patient’s outcomes. Therefore; effective and integrated intervention using scientific research is vital to tackle such fatal and devastating public health problem. Hence, advanced stastical studies on the predictors of diarrhea among under-5 children have paramount importance.

Currently, the healthcare sector produces huge data about patients and disease diagnosis, and when such data is well processed and analyzed with robust methods it provides important knowledge and strong evidence generation that can be used proficiently in decision-making, healthcare management, disease detection, and diagnosis. Moreover, previous studies have provided ample evidence on the socio-demographic and socio-economic related, maternal-related, environmental-related, and feeding-related factors associated with diarrhea using classical methods and traditional regression models.^6,10–14 There was a lack of literature exploring the application of ML models for predictive purposes in this specific domain. The potential advantages of ML methods over the traditional models include their ability to handle complex nonlinear data, operate without preexisting assumptions, and capture intricate relationships among predictors.^15,16 The ML modeling could inform the development and implementation of m-health applications and digital health systems targeting the prevention and management of the disease under study. Besides, ML algorithms offer several benefits for classification and prediction, such as automation, pattern recognition, adaptability, scalability, objectivity, handling non-linearity, feature selection, and generalization, making them powerful tools for addressing real-world problems and facilitating data-driven decision-making.¹⁷ In this research, five advanced ML techniques such as support vector machine, RF, Gaussian naives Bayes, logistic regression, and decision tree were employed to predict the occurrence of diarrhea using demographic health survey data. The study aimed to predict diarrhea episodes and identify its predictors using state-of-the-art ML models. The findings will provide evidence for policymakers to design evidence-based programs and integrated interventions that target the prevention of diarrhea and safeguard the health of vulnerable subgroups, particularly among under-5 children in Ethiopia.

Methods

Data source

A nationally representative cross-sectional 2016 Ethiopian Demographic and Health Surveys (EDHS) were conducted and the data were obtained through a formal written request to the DHS program website (https://www.dhsprogram.com/).¹⁸ As a member of the global demographic and health surveys program, Ethiopia participated in the EDHS 2016 for the fourth time, which was conducted using a cross-sectional study design from January 18 to June 27, 2016. This countrywide representative household survey used a multi-stage stratified sampling technique,^19,20 based on the 2007 census of Ethiopia to recruit respondents from 624 clusters spread throughout nine regions and two administrative cities (187 urban and 437 rural). The unit of analysis was under-5 children with a total sample size of 10,006 chosen from 645 clusters in Ethiopia. The participant was all reproductive women who had at least one under five age child proceeding to the survey. A total of 10,006 under-5 children would make up the sample size for this study. Since the EDHS data contains several missing values for outcome variables, a total weighted of 9501 under-5 children were included and we have analyzed 29 different features in the final analysis. After performing a statistical test (such as Little’s test), we determined that the missing data was completely at random. As a result, we decided to handle the missing data through two approaches: For the outcome variable, we opted to manage the missing data by deleting those observations and for the missing independent variables, we chose to impute the values.

Population of the study

All children aged under-5 years in Ethiopia were the source populations for this study, whereas all children aged under-5 years in the selected enumeration areas (EAs) and whose diarrhea status recorded were the study populations.

Eligibility criteria

Those under-5 years old children who had a complete data on the 2016 EDHS data set were included for the current study, but children having a missing value exceeded 30% were excluded from this study.

Sampling procedures

The sampling procedure for the EDHS involved two stages (multi-stage) sampling techniques. In the first stage, a stratified sample of 645 enumeration areas (202 in urban areas) was selected using a probability proportional to size method. Within each stratum, a predetermined number of enumeration areas were independently selected based on their size. A listing procedure was then carried out in the selected enumeration areas to list all households. In the second stage, a fixed number of households were selected from each selected enumeration area using equal probability systematic sampling. More detailed information on the sampling procedure can be found in the EDHS reports available on the Measure DHS website (https://www.dhsprogram.com/) for each specific survey.¹⁸ Sample in this study were selected children who had recorded data on diarrhea reports, resulting in a total sample size of 9501 after accounting for their respective weights.

Study variables and measurements

Outcome variable

This study has considered the children recode or kid’s record (KR file) from 2016 EDHS dataset.²⁰ The dependent variable is “a child had diarrhea or not prior to 2 weeks of the survey”, which was measured as a binary outcome as absence of diarrhea (coded as 0) or presence of diarrhea (coded as 1).

Independent variables

Age Group: Current age of child is re-coded in to five categories with values of “0” for <6 months, “1” for 6–12 months, “2” for 13–23 months, “3” for 24–35 months, “4” for 36–47 months, and “4” for 48–59 months”.⁶ Anemia: recoded in two categories with a value of “0” for non-anemic, “1” for anemic using WHO defined hemoglobin levels less than 11 g/dl. Wealth Index: The datasets contained wealth index that was created using principal components analysis coded as “poorest”, “poorer”, “Middle”, “Richer”, and “Richest in the EDHS data set·” For this study we recoded it in to three categories as “poor” (includes the poorest and the poorer categories), “middle”, and “rich” (includes the richer and the richest categories).²¹ Occupation: Re-coded in two categories with a value of “0” for not working, and “1” for working. Media exposure: A composite variable obtained by combining whether a respondent reads newspaper/magazine, listen to radio, and watch television with a value of “0” if women were not exposed to at least one of the three media, and “1” if a woman has access/exposure to at least one of the three media. Educational status: this is the minimum educational level a woman achieved and coded into four groups with a value of “0” for no education, “1” for primary education, and “2” for secondary and “3” for higher educational level.^21,22 Source of drinking water: By using the DHS guide it was recoded into two categories as “unprotected” and “protected source”.^18,21,23 Family size: Recoded in to two categories as 1–4, and greater than or equal 5.²² The altitude of the cluster categorized as high and low altitude using 2500 m as reference.²¹ Type of place of residence: The variable place of residence recorded as rural and urban in the dataset was used without change. Type of toilet facility: using the DHS guide following the WHO/UNICEF Joint Monitoring Programmed on Water and Sanitation guidelines it was recoded in two categories as improved if flush - to somewhere else, pit latrine - without slab/open pit, bucket toilet, and hanging toilet/latrine and unimproved if flush - to piped sewer system, flush - to septic tank, flush - to pit latrine, flush - don’t know where, pit latrine - ventilated improved pit, and pit latrine - with slab.^18,23,24 Regarding stunting and wasting we have compute and classify based on a height/age Z-score < − 2SD, and a weight/height Z-score <− 2SD were considered to be stunted and wasted respectively.²⁵ Distance to health facility were recoded as long if it takes greater than 30 min and not long if it takes less than 30 min from the place of residence to the nearby health facility.²¹ The selection of these predictors was based on information from available literature on the thematic area.^6,11,26–28

Data preprocessing and analytic strategies

Before constructing a prediction model, it is crucial to preprocess raw data for analysis to enhance the model’s ability to make accurate predictions. Data preprocessing encompasses various techniques, including data cleaning, feature engineering, dimensionality reduction, and data splitting.²⁹ To ensure the representativeness of the data and obtain reliable estimates with increased precision, reduced standard error, and to account for unequal selection probabilities resulting from the sampling design, the data underwent weighting before statistical analysis. Continuous data was categorized using the binning discretization method. Additionally, descriptive statistics were conducted on the background characteristics of the participants to provide a comprehensive overview of the EDHS data set sample. The specific methodology employed in this study is presented in Figure 1.

Figure 1.

Study workflow diagram.

Data cleaning

The initial stage of data preprocessing involves data cleaning, which includes the identification and removal of outliers, handling missing values, and addressing imbalanced categories in the outcome variable. We explored different approaches for managing missing data in ML, such as deletion, imputation, model-based imputation, and leveraging domain-specific knowledge. Taking into account factors such as the nature of missingness, the amount of missing data, assumptions, and the ML algorithm employed, we decided to handle missing values in our dataset using complete case deletion. This approach was chosen due to a large proportion (exceeding 30%) of missing values that were completely random.³⁰ To identify outliers, we utilized various visualization techniques, including scatter plots, box plots, and histograms. These methods allowed us to identify data points that significantly deviated from the overall pattern. Furthermore, we assessed multicollinearity by examining the correlation matrix, considering a correlation value above 0.8 between two variables as indicative of high correlation.³¹

Data balancing

Addressing imbalanced data is another important aspect of data cleaning. Imbalanced data, where one class is significantly underrepresented, poses a challenge in ML as it can result in reduced classification accuracy, especially for instances belonging to the minority class. ML models trained on imbalanced data are typically biased toward the majority class and fail to predict cases that are rare/minority class.³² To overcome this issue, researchers have developed various techniques. In this study, we employed four balancing methods³³: under-sampling, over-sampling, adaptive synthetic sampling (ADASYN), and synthetic minority oversampling technique (SMOTE). Initially, we trained our chosen ML algorithms using the Imbalanced data. Subsequently, we explored different methods such as under-sampling, over-sampling, ADASYN, and SMOTE to balance the data for training the models. The selection of these techniques was depends on nature of the class imbalance, computational efficiency, domain knowledge, sensitivity to outliers, and model complexity. We then evaluated the performance of the models by comparing accuracy and AUC metrics. In cases where one algorithm demonstrated higher accuracy but lower AUC compared to another, we considered the AUC value for Imbalanced data and the accuracy value for balanced data. Accuracy is a suitable metric for balanced classes, while AUC is valuable for imbalanced datasets or when the relative cost of false positives and false negatives is unknown. It is recommended to consider both accuracy and AUC, along with other relevant metrics, to gain a comprehensive understanding of the model’s performance and make informed comparisons between different ML algorithms. Considering these factors, the SMOTE balancing technique exhibited superior performance in balancing our data and improving model prediction accuracy.

Feature engineering

Feature engineering involves transforming raw data into more suitable features for predictive models. In this study, categorical variables were converted into numeric values using one-hot coding, and label encoding was used to assign unique numbers to each category of variables. Dimensionality reduction techniques were applied to reduce the number of input variables, aiming to create a simpler and more effective predictive model for new data.³⁴

There are two approaches to dimension reduction: feature selection and feature extraction, with the latter being more appropriate for image processing.³⁴ Feature selection involves choosing the most relevant independent variables that have the greatest impact on predicting the target variable. In our dataset, feature selection is the appropriate method. Various well-known feature selection methods were explored, including Lasso, PCA, wrapper methods (such as forward selection, backward elimination, and recursive feature elimination), correlation-based feature selection, and chi-square test. Their performance was compared using evaluation metrics such as accuracy, sensitivity (recall), specificity, F1 score, and area under the curve (AUC).³⁵ Based on this analysis, Boruta was identified as the most effective feature selection method. Boruta is a wrapper-based technique that utilizes the RF classifier algorithm, known for its unbiased and consistent performance in selecting key variables.^36,37 Combining Boruta with the RF classifier offers several advantages, including enhanced feature selection, robustness against noise and irrelevant features, reduced bias in feature importance, and improved interpretability. This combination refines the feature selection process, resulting in improved model performance, reduced over fitting, and increased interpretability. However, there are challenges and limitations associated with their use such as computationally intensive, especially for large datasets or high-dimensional feature spaces, less intuitive and transparent, struggle with complex, non-linear relationships between input features, and not scale well to extremely large or high-dimensional datasets, which were overcome using techniques such as L1 or L2 regularization, cross-validation, maintaining an independent test set, parallel processing, analyzing feature importance stability across multiple runs or subsets, recursive feature elimination, balancing false positives and false negatives, and conducting principal component analysis.³⁸

For data splitting, an 80/20 split method was employed, where 80% of the samples (7601 respondent data) were used for training and the remaining 20% (1900 samples) were used for testing the model. However, for model training, a tenfold cross-validation method was utilized, as it avoids excessive data wastage, which is advantageous when the sample size is small.³⁹

Model selection and development

After splitting the data into training and testing sets, we proceeded to select suitable models for training. Since the target variable involved classification, specifically binary classification for diarrhea status (child has diarrhea or no diarrhea), we chose five state-of-the-art ML algorithms to assess their predictive capabilities. These algorithms were selected based on previous research utilizing ML techniques for classification tasks on EDHS data.^15,40–42 Moreover, the selection of these algorithms were depend on their scalability, interpretability, features number, computational efficiency, data characteristics, type of problem, robustness to noise/outlier, accuracy, bias-variance trade off, and domain expertise. In this study, we utilized the scikit-learn version 1.3.2 packages in Python, implemented within Jupyter Notebook, to employ ML algorithms. The descriptions of the five algorithms are as follows:

Support vector machine (SVM)

Support vector machine (SVM) is a set of supervised learning methods used for classification, regression, and outlier detection. SVMs are preferred when dealing with high-dimensional spaces, robustness to outliers, nonlinearity, margin maximization, memory efficiency, and small to medium-sized datasets are important considerations for the problem at hand.⁴³ However, SVMs may have limitations in terms of scalability to large datasets and computational efficiency, especially when using non-linear kernels.⁴⁴ Besides, SVMs may not perform well when the dataset is imbalanced, or when the classes are overlapping and not well-separated.

Gaussian Naïve Bayes (GNB)

Gaussian Naïve Bayes (GNB) is an algorithms built based on Bayes theorem which has two basic assumptions.⁴⁵ The first one is every pair of features should be independent of each other and the second assumption is the feature must have an equal contribution to the outcome prediction. GNB is preferred when efficiency, simplicity, handling continuous features, small training sets, text classification, and the feature independence assumption are important considerations for the problem at hand.⁴⁶ However, GNB may not perform well in cases where the two assumptions are severely violated. It may struggle with datasets where the features have strong dependencies or when the decision boundary is complex.^44,45

Decision tree (DT)

A DT is a non-parametric technique that classifies a data set based on the problem’s predictive structure. DT is highly interpretable, efficiently capture nonlinear relationships, handle both categorical and numerical features, relatively robust to outliers and noisy data, handle missing values by utilizing surrogate splits or imputation techniques, and can handle large datasets efficiently.⁴⁷ For this study, because of these advantages we have employed DT algorithm to predict the status of diarrhea among under-5 children in Ethiopia. However, DT also has limitations such as prone to over fitting, struggle with capturing certain complex relationships that require more sophisticated algorithms, and can be sensitive to small changes in the data, leading to different tree structures.⁴⁴

Logistic regression (LR)

Logistic regression (LR) is a supervised ML algorithm used to solve classification issues. It is a parametric method that assumes a Bernoulli distribution of the target variable and the independence of the observations.⁴³

Random forest (RF)

Random forest (RF) is a supervised ML that can be used for classification, regression, and dimension reduction purposes. It is a versatile algorithm used for huge amounts of data and overcoming noise. RF uses an error-minimizing technique to select the variables to split into groups. RF are preferred when improved predictive performance, reduced bias, reduction of variance, robustness to noise and outliers, feature importance, and handling high-dimensional data are important considerations for the problem at hand.^48,49 However, RF has some limitations such as a black-box model, making it less interpretable or more difficult to interpret compared to individual DT; the ensemble nature of RF makes it challenging to trace the decision-making process. Besides, RF may not perform well on datasets with strong linear relationships.^44,45

Model training and evaluation

Once splitting the data into training and testing sets, we selected suitable models for training, focusing on classifiers appropriate for the categorical target variable. The dataset involved binary classification for diarrhea, so we utilized five ML algorithms: LR, RF, SVM, GNB, and DT classifiers.

Once the models were selected, we trained them using both balanced and imbalanced data. Then best predictive model was chosen and trained using balanced training data for the final prediction on unseen test data. To assess the performance of the final model, we used a confusion matrix and receiver operating characteristic (ROC) curve, employing metrics such as accuracy, sensitivity/recall, specificity, F1 weighted score, and area under the curve (AUC). AUC was considered the primary performance metric as it provides an overall evaluation of the model’s performance at various classification thresholds. The confusion matrix allowed us to extract one-dimensional performance metrics, including True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN).³² Moreover, median percent error and measure of bias were estimated.

Accuracy: The proportion of correctly classified instances over the total number of instances. It is appropriate when the classes are balanced, misclassification costs are symmetric, and there is no significant class imbalance.

Precision: The proportion of true positive predictions over the total number of positive predictions. It indicates the model’s ability to correctly identify positive instances.

Recall (Sensitivity or True Positive Rate): The proportion of true positive predictions over the total number of actual positive instances. It measures the model’s ability to capture positive instances. Recall is particularly useful in imbalanced class settings, where the cost of missing positive instances is high and in applications where early detection or risk mitigation is critical.⁵⁰

F1 weighted Score: The harmonic mean of precision and recall. The F1 weighted score is particularly useful in imbalanced class settings, where precision and recall need to be considered equally, and in situations where a trade-off between precision and recall is important.

Specificity (True Negative Rate): The proportion of true negative predictions over the total number of actual negative instances. It measures the model’s ability to correctly identify negative instances. Specificity is particularly useful in imbalanced class settings, where the cost of false positive errors is high and in applications where risk mitigation or minimizing false positive rates is critical.

ROC Curve (Receiver Operating Characteristic Curve): A graphical representation of the trade-off between true positive rate and false positive rate at various classification thresholds. The ROC curve is particularly useful in imbalanced class scenarios, for threshold selection, and for comparing models. Its threshold insensitivity and visual representation make it a valuable tool for evaluating the overall performance of classification models.⁵¹

The Area under the ROC Curve (AUC-ROC) is commonly used as a metric to assess the model’s discriminative power.

Confusion Matrix: A tabular representation that shows the counts of true positives, true negatives, false positives, and false negatives. It provides a detailed breakdown of the model’s performance. The confusion matrix is particularly useful for understanding error types, analyzing performance on imbalanced classes, conducting error analysis, comparing models, and assessing the impact of threshold selection.

Ultimately, the choice of evaluation metric should be driven by the specific context requirements, trade-off between different evaluation metrics, benchmark and standard on the same field, model interpretability, problem type, data characteristics, and goals of the task at hand. Therefore, it’s crucial to carefully evaluate and select the metrics that best align with the problem type, data characteristics, and objectives to effectively assess the model’s performance.^52–54

In addition to the standard metrics, tenfold cross-validation techniques were employed to further evaluate the model’s performance. Tenfold cross-validation involves dividing the data into ten subsets and training and evaluating the model ten times, each time using a different combination of nine subsets for training and one subset for evaluation.⁵⁵ The research also carried out a comprehensive examination of hyper parameters with the aim of enhancing and optimizing the model’s performance. Various methods such as grid search, random search, and Bayesian optimization were systematically employed to discover the most effective hyper parameter configurations. The choice of these methods is depend on various factors such as the size of the search space, the available computational resources, and the desired balance between exploration and exploitation. Grid search is a simple and exhaustive method but can be computationally expensive. Random search is less intensive but may require more iteration. Bayesian optimization is efficient and effective for complex search spaces but may require additional setup and computational resources. Suitability of each tuning method also depends on the specific ML algorithm being used and the characteristics of the dataset. Experimentation and evaluation of different methods on the validation set is recommended to identify the most effective approach for hyper parameter tuning.⁵⁶ Therefore, the authors tried all techniques considering their advantages to select the best tuning technique based on their performance metrics. Additionally, to enhance the precision and reliability of the model used in this study, calibration was conducted. By fine-tuning the model through calibration, its ability to accurately predict the desired outcome was significantly improved.

Model interpretability

Researchers have emphasized the potential of combining SHAP (SHapley Additive exPlanations) values and association rule mining for various purposes.^57,58 Association rule mining is suitable for uncovering hidden patterns and connections in the data, while SHAP analysis is more appropriate for understanding how different features impact on the model predictions and a widely used method in ML for interpreting predictions and understanding feature importance.^57,59 To gain a comprehensive understanding of the data and analyze factors influencing diarrhea prediction, we employed multiple techniques. Firstly, we calculated average SHAP values to assess the overall impact of each feature on the model’s predictions, providing insights into the relative importance of variables. It assigns a numerical value, known as a SHAP value, to each feature, indicating its contribution to predictions. Positive values signify positive contributions, negative values indicate the opposite, and the magnitude represents the strength of influence. SHAP analysis enhances transparency and interpretability, offering a global perspective on feature importance and explaining individual predictions.^60–62 Following that, we utilized a waterfall plot to visually represent the cumulative effects of these variables, highlighting their contributions to the overall prediction.⁶³

Association rule mining

In this study, we utilized association rule analysis with the Apriori algorithm in R software to identify specific predictor variables associated with diarrhea. The aim of this analysis was to uncover connections between categorical attributes and diarrhea among under-5 children in Ethiopia, as ML algorithms do not inherently reveal the strength of associations with diarrhea for different categories. By examining frequently occurring patterns and identifying dependencies among attributes, our goal was to understand the relationships between different attributes and their level of confidence in predicting diarrhea. To achieve this, we employed If/Then statements to reveal these associations.⁶⁴ The If/Then association rule is a pair of attributes (X, Y) expressed as X->Y, where X is the antecedent and Y is the consequent. This rule signifies that if X happens, then Y would also happen. The relationship between X and Y attributes can be categorized based on the lift value. A lift value of 1 indicates an uncorrelated rule, meaning that X and Y appearing at the same time belong to independent random events and have no special significance. If the lift value is less than 1, it indicates a negative correlation rule, where the occurrence of X reduces the occurrence of Y. On the other hand, if the lift value is greater than 1, it indicates a positive correlation rule, where the occurrence of X promotes the occurrence of Y.⁶⁵

Statistical analysis used in this study

In this study we have employed a range of statistical analysis techniques to gain insights from their data and develop effective models. Exploratory data analysis, such as examining descriptive statistics and visualizing data patterns, is typically the first step to understand the characteristics of the dataset. Regression analysis is commonly used to model the relationship between variables, while classification techniques are applied to categorize data into different groups. Model evaluation metrics, like accuracy, AUC, and F1-score, are crucial for assessing the performance of the developed machine learning models; while techniques like cross-validation help estimate the model’s generalization ability. Additionally, SHap analysis and association rule mining technique were employed. By integrating these diverse statistical analysis approaches, researchers can extract meaningful insights, build robust machine learning models, and draw reliable conclusions from their studies.

Results

Socio-demographic characteristics of study participants

From a total of 10,006 under-5 children data retrieved, 9501 were met the inclusion criteria and counted in in the final analysis. Of those children, 4325 (89.1%) male child had no experienced diarrhea. Nearly two-third of mothers were not attended a formal education (64.1%) and 591 (9.7) of these children experienced diarrhea. Nearly one-fifth of children aged 6-11 month (19.1 %) had experienced diarrhea (Table 1)

Table 1.

Socio-demographic characteristics of respondents in Ethiopia, 2016 (N = 9501).

Variables	Categories	Diarrhea
		No	Yes
		Count (%)	Count (%)
Residence	Urban	1652 (91.1)	162 (8.9)
Residence	Rural	6880 (89.5)	807 (10.5)
Age of child	<6 months	1149 (92.0)	100 (8.0)
	6-11 months	617 (80.9)	146 (19.1)
	12-23 months	1528 (83.8)	296 (16.2)
	24-35 months	1625 (88.8)	204 (11.2)
	36-47 months	1682 (92.2)	142 (7.8)
	48-59 months	1931 (96.0)	81 (4.0)
Sex of child	Male	4325 (89.1)	527 (10.9)
Sex of child	Female	4207 (90.5)	442 (9.5)
Mothers educational level	No formal education	5498 (90.3)	591 (9.7)
	Primary	2097 (88.5)	273 (11.5)
	Secondary	599 (89.5)	70 (10.5)
	Higher	338 (90.6)	35 (9.4)
Family size	<=5	3237 (89.2)	393 (10.8)
Family size	>=6	5295 (90.2)	576 (9.8)
Wealth index	Poor	4633 (90.3)	497 (9.7)
	Middle	1152 (88.01)	157 (11.99)
	Rich	2747 (89.7)	315 (10.3)
Mothers’ occupation	Not working	5121 (90.3)	547 (9.7)
Mothers’ occupation	Working	3411 (89.0)	422 (11.0)
Number of under-5 years children in household	<=2	7678 (89.9)	858 (10.1)
Number of under-5 years children in household	>=3	854 (88.5)	111 (11.5)
Number of living children	1-3	4233 (89.0)	525 (11.0)
	4-6	3043 (89.9)	342 (10.1)
	Above 6	1256 (92.5)	102 (7.5)

Environmental characteristics of respondents

In this study, more than half of respondents (55.9%) had to travel long distances (taking 30 min or more) to access health services. Among those who traveled long distances, 10.2% of children had experienced diarrhea. Furthermore, more than three-fourth of households (87.7%) had unimproved toilet facilities, and among these households, 10.3% of children had experienced diarrhea. In terms of child waste disposal, the majority (89%) of waste disposal practices was found to be unsafe, and 10.1% of children who had unsafe waste disposal experienced diarrhea (Table 2).

Table 2.

Environmental characteristics of the respondents in Ethiopia, 2016 (N = 9501).

Variables	Categories	Diarrhea
		No	Yes
		Count (%)	Count (%)
Type of toilet facility	Improved	1062 (90.8)	108 (9.2)
Type of toilet facility	Not improved	7470 (89.7)	861 (10.3)
Source of drinking water	Unprotected	4532 (89.8)	514 (10.2)
Source of drinking water	Protected	4000 (89.8)	455 (10.2)
Child’s stools disposal	Not safe	7598 (89.9)	851 (10.1)
Child’s stools disposal	Safe	934 (88.8)	118 (11.2)
Covered by health insurance	No	8068 (89.7)	923 (10.3)
Covered by health insurance	Yes	464 (91.0)	46 (9.0)
Distance to health facility	Long	4772 (89.8)	540 (10.2)
Distance to health facility	Not long	3760 (89.8)	429 (10.2)
Child lives with whom	Respondent	8371 (89.7)	961 (10.3)
Child lives with whom	Lives elsewhere	161 (95.3)	8 (4.7)

Nutritional and co-morbid characteristics among under-5 children

Among the total respondents, majority of them (93.5%) of children were vaccinated for rotavirus. However, 5613 (59 %) and 8393 (88.3%) didn’t take Vitamin-A supplement and intestinal parasites’ drug in the previous 6 months respectively. Regarding nutritional status, about 922 (9.7 %) of the respondents were wasted (height for weight Z score < −2 standard deviation) (Table 3).

Table 3.

Nutritional and co-morbid characteristics of under-5 children in Ethiopia, 2016.

Variables	Categories	Diarrhea
		No	Yes
		Count (%)	Count (%)
Rota virus	Not vaccinated	536 (86.6)	83 (13.4)
Rota virus	Vaccinated	7996 (90.0)	886 (10.0)
Currently breastfeeding	No	2950 (91.1)	288 (8.9)
Currently breastfeeding	Yes	5582 (89.1)	681 (10.9)
Stunting	Normal	4933 (89.6)	571 (10.4)
Stunting	Stunted	3599 (90.0)	398 (10.0)
Drug use for intestinal parasites	No	7563 (90.1)	830 (9.9)
Drug use for intestinal parasites	Yes	969 (87.5)	139 (12.5)
Duration of breast feeding	Ever breastfeed	8187 (89.6)	947 (10.4)
Duration of breast feeding	Never breastfeed	345 (94.0)	22 (6.0)
Wasting	Normal	7727 (90.1)	852 (9.9)
Wasting	Wasted	805 (87.3)	117 (12.7)
Vitamin A supplement	No	5027 (89.6)	586 (10.4)
Vitamin A supplement	Yes	3505 (90.1)	383 (9.9)
Short, rapid breaths (acute respiratory infection)	No	8229 (91.1)	799 (8.9)
Short, rapid breaths (acute respiratory infection)	Yes	303 (64.1)	170 (35.9)
Had anemia	No	5383 (90.1)	591 (9.9)
Had anemia	Yes	3149 (89.3)	378 (10.7)

Prevalence of diarrhea among under-5 children in Ethiopia

From a total of 9501 under-5 children were included in the analysis, the prevalence of diarrhea prior to 2 weeks of the 2016 survey was found to be 969 (10.2%) (Figure 2).

Figure 2.

Prevalence of diarrhea among under-5 children in Ethiopia in 2016 using Imbalanced data set (a) and balanced data with SMOTE techniques (b).

Machine learning analysis of diarrhea among under-5 children

Feature selection

Figure 3 showcases the Boruta algorithm graph, visually indicating the significance of variables important for model prediction. Important variables are highlighted in a prominent green color, while unimportant variables are displayed in red. Yellow indicates tentative variables that require further investigation to fully understand their impact on the model’s predictions.

Figure 3.

Feature selection using Boruta algorithm method.

During our analysis, we excluded 13 variables that were deemed unimportant by the Boruta algorithm. Additionally, we considered a Rota vaccination as a tentative variable. As a result, we selected the variables identified as important by the Boruta algorithm to predict the diarrhea status. Using this approach, a total of eleven variables out of the initial twenty-four were chosen as the top important features for constructing the model. This allowed us to gain valuable insights into the underlying patterns in the data using association rule mining.

Data balancing

We employed four data balancing techniques, including under-sampling, over-sampling, SMOTE, and ADASYN, and evaluated their performance using accuracy and AUC values. In the case of Imbalanced data, the SVM model achieved an accuracy of 90%. The RF model outperformed other algorithms with AUC values of 64%, 79%, 78%, and 82% for under sampling, oversampling, ADASYN and SMOTE techniques respectively. Among all the balancing techniques, SMOTE emerged as the superior and most effective balancing technique for the final prediction. Table 4 provides a comparison of different data balancing techniques, including AUC and accuracy values for the Imbalanced dataset. For a comprehensive visual representation comparing the performance of ML algorithms with each data balancing technique, please refer to supplemental material Figure 1.

Table 4.

Comparison of imbalanced data handling techniques using accuracy and AUC values.

Algorithms	Evaluation metrics	Imbalanced data	Under sampling	Over sampling	SMOTE	ADASYN
SVM	Accuracy	90	60	66	66	65
SVM	AUC	57	62	73	74	71
GNB	Accuracy	88	58	61	59	61
GNB	AUC	66	63	65	67	63
LR	Accuracy	90	59	62	60	62
LR	AUC	66	65	66	67	65
DT	Accuracy	87	55	71	72	70
DT	AUC	52	41.1	77	81	76
RF	Accuracy	88	59	72	74	71
RF	AUC	59	64	79	82	78

Note: AUC-area under curve, ADASYN-Adaptive Synthetic Sampling, DT-decision tree, RF-random forest, SMOTE-Synthetic Minority Over-sampling Technique, GNB-Gaussian naïve Bayes, SVM-support vector machine, LR-logistic regression.

Model development and performance evaluation metrics of ML algorithms

To assess and compare the performance of the algorithms in predicting diarrhea among children under the age of 5 in Ethiopia, various metrics such as accuracy, precision, recall, F1 score, and AUC value were utilized. These metrics provided insights into the algorithms’ overall correctness, their ability to predict positive and negative instances accurately, and their discriminative power. The researchers conducted a thorough evaluation using these performance metrics to determine the effectiveness of the algorithms. Among the three tuning techniques evaluated, grid search emerged as the best, achieving the highest precision, recall, and F1 score. Based on the evaluation metrics, the RF classifier was identified as the top ML algorithm for classifying the diarrhea status (Figure 4).

Figure 4.

Performance evaluation metrics of the selected algorithm after data balancing and grid search tuning.

Among all the algorithms, the RF classifier performed is the best predictive model, achieving an AUC of 0.916 followed by decision tree with an AUC value of 0.827, which are considered to be an acceptable ROC values. Conversely, the AUC value for logistic regression was 0.689, indicating no discrimination and suboptimal performance. For a comprehensive comparison of the five ML algorithms and their performance across the three tuning techniques, please refer to Table 5.

Table 5.

Accuracy and AUC value of ML algorithms using three hyper parameter tuning techniques.

Algorithm	Grid search		Random search		Bayesian optimization
Algorithm	Accuracy	AUC	Accuracy	AUC	Accuracy	AUC
SVM	75	0.762	71	0.750	70	0.744
GNB	59	0.713	60	0.703	61	0.685
LR	60	0.689	60	0.685	59	0.678
DT	73	0.827	71	0.818	70	0.816
RF	75	0.916	74	0.867	72	0.839

Figure 5 displays the ROC curve analysis of ML algorithms before and after hyper parameter tuning. The data was balanced using the SMOTE data balancing technique, and additional optimization was performed through hyper parameter tuning.

Figure 5.

ROC curve analysis of machine learning algorithms of SMOTE balanced data before tuning (a), and after hyper parameter tuning (b).

Model interpretability

SHAP value interpretation

Based on the findings presented in Figure 6, the mean SHAP value analysis provided valuable insights into the relative importance of different features in the classification model. Factors such as child age, acute respiratory infection, number of living children, and drug use for intestinal parasites were identified as highly influential, significantly impacting the model’s predictions. On the other hand, all the other features as indicated by their low mean SHAP values contributes less to the model’s decision-making process and have limited importance in predicting the outcome.

Figure 6.

A mean SHAP value report.

Figure 7, the waterfall plot, revealed the hierarchical importance of features in predicting the target variable. The plot highlighted that region had the child age has positive impact on the prediction, followed by wealth index and residence. Higher values of these features tended to increase the predicted outcome.

Figure 7.

Waterfall plot.

In the SHAP summary plot, the colors represent the magnitude and direction of the feature’s impact on the model’s output. The “hot” colors, such as red, indicate that the feature has a strong positive influence on the model’s prediction, meaning that higher values of that feature are associated with a higher predicted value. Conversely, the “cool” colors, such as blue, suggest that the feature has a strong negative influence, where higher values of the feature are associated with a lower predicted value.

The intensity of the color represents the magnitude of the feature’s impact. Darker shades of red or blue indicate a stronger positive or negative influence, respectively, while lighter shades represent a weaker impact.

Association rule result

Through the utilization of the Apriori algorithm, our study identified influential association rules based on lift values and confidence. These rules provided valuable insights into the probability of diarrhea among under-5 children in Ethiopia. Factors such as wealth index, number of living children, occupation, source of drinking water, drug use for parasite, age of child, wasting, and educational status consistently appeared in these association rules, indicating their strong association with diarrhea likelihood. These factors significantly influence the probability of diarrhea and should be considered in initiatives aimed at enhancing child health in the region. A total of 58 rules were generated, and the top seven association rules are presented below:

Rule 1: If children between 6 to 23 months old, wasted, belonging to households with 4 to 6 living children, using an unimproved source of drinking water, and with mothers having no education, the probability of diarrhea is 91.7% (lift value = 2.3).

Rule 2: If children from households with a poor wealth index, aged 6 to 23 months, not taking medication for intestinal parasites, having fewer than two living children, and wasted, the probability of diarrhea occurrence is 90.9% (lift = 2.3).

Rule 3: If children aged 6 to 23 months, not taking medication for intestinal parasites, and having fewer than two living children, the probability of diarrhea occurrence is 87% (lift = 2.2).

Rule 4: If children whose mothers have no education, aged between 6 to 23 months, wasted, experiencing acute respiratory infection, and using an unprotected source of drinking water, the probability of diarrhea occurrence is 86.7% (lift = 2.2).

Rule 5: If children from parents with a poor wealth index, aged between 6 to 23 months, wasted, experiencing acute respiratory infection, and using an unprotected source of drinking water, the probability of diarrhea occurrence is 86.7% (lift = 2.2).

Rule 6: If a child whose mothers has no education, with a poor wealth index, and wasted the probability of diarrhea is 83.3% (lift = 2.1).

Rule 7: If children from rural areas, not vaccinated against Rota, from families with a poor wealth index, and having more than three under-5 children, the probability of diarrhea is 82.8% (lift = 1.9).

Discussion

The aim of this study was to develop and compare the most effective ML algorithm for predicting diarrhea in children under the age of 5 in Ethiopia. By utilizing robust data science techniques, this research contributes to the advancement of knowledge in diarrhea prediction. These findings have the potential to pave the way for the development of automated screening tools, aid policymakers in making informed decisions, facilitate the design of suitable interventions, and provide decision support systems to healthcare providers for diagnosing and managing diarrhea effectively. The current study showed that 10.2% of children had experienced diarrhea within 2 weeks prior to the 2016 EDHS survey. The finding was in line with a meta-analysis conducted in Africa, which showed that 10.3% of under-5 children had diarrhea.⁶⁶ In contrast, the present finding was lower than the previous traditional logistic regression approach studies conducted in Ethiopia,^14,67–69 Cameroon (23.8%),⁷⁰ and Yemen (29.07%).⁷¹ This could be environmental and infrastructure discrepancy across countries.^26,68 Moreover, the national immunization coverage has been increased from 24% to 44%, which plays a vital role in diarrhea prevention and reduce the incidence of the diarrheal disease.⁷²

Five supervised ML algorithms were applied to predict diarrheal status among under-five children using socio-demographic, environmental, nutrition, and other comorbid related data set of 2016 EDHS. To increase model prediction accuracy and generalizability, the above models were chosen to advance and confirm the best predictive model using the significant predictor variables. To maximize the predictive accuracy of the final model, data balancing techniques were employed. After evaluating the performance metrics of the balanced data, the RF model exhibited the best overall performance with an evaluation metrics of accuracy (93.2%) and AUC (91.6%). The use of RF classifier for predicting diarrhea has implications by providing accurate predictive models, insights into risk factors and mechanisms, identification of vulnerable subgroups, and the potential for integrating ML into healthcare systems. These implications pave the way for targeted interventions, personalized healthcare approaches, and improved health outcomes for children affected diarrhea. Despite there was a disparity in the value of performance evaluation metrics, the current metrics performance was supported by a studies conducted so far in Japan in 2019⁷³ and Afghanistan in 2021.⁷⁴ The possible justification could be the variation in size of data set and the number of attributes used in predicting the best model since the size of the data set and number of predictors has its own impact on model prediction and performance evaluation metrics values.⁷⁵

The analysis of the mean SHAP value report and waterfall plot yielded valuable insights into the importance of different factors in a classification model for predicting diarrhea status. Features such as residence, educational status, child’s age, mother’s occupation, Rota vaccination status, and wealth index were identified as highly influential, significantly affecting the model’s predictions. These findings provide guidance for targeted interventions and policy decisions, contributing to improved health and well-being of under-5 children in Ethiopia. By validating existing knowledge and evaluating model effectiveness, these insights enable more accurate and impactful interventions for the health of children in the region.

Regarding this study, the RF model identified several significant predictors for diarrhea among under-5 children. The top important predictors includes place of residence, wasting, mother’s occupation, mother’s educational status, number of living children, age of child, and mother’s wealth index and drug use for intestinal parasite were found to be predictors of diarrhea.

Another aim of this study was to identify the top predictors of diarrhea among under-5 children. To accomplish this, the author utilized the Boruta algorithm to select important features. Out of a total of 24 features included based on the literature; the study identified 11 predictors as important feature to predict diarrhea. This suggests that ML models may uncover new variables or insights not captured by conventional regression models, which could be valuable for policy decision-making and clinical practices.

The other aim of the study was to use association rule mining with the a priori algorithm to identify patterns and associations between independent predictors and the outcome variable. We have selected the top seven rules generated by the best model which has most frequently association with a high probability of diarrhea.

This association rule mining revealed that residence was found to be significant predictors of diarrhea among under-5 children. Accordingly, children residing in rural areas were more likely to experienced diarrhea compared to the urban areas. This finding was similar with studies conducted in different localities of Ethiopia and Zambia.^14,27,68,69 This may perhaps due to rural residents have less access to clean water, poor sanitation, and low level of hygienic practice for their child nurturing.

Another important predictor identified was child age. Children whose age is greater than 6 months are high risk for diarrhea compared to child aged less than 6 months. This finding is consistent with previous studies done in Ethiopia.^14,76,77 This might be due to the initiation of contaminated weaning foods, risk of ingesting contaminated materials, and loss of inborn immunity or maternal antibodies increases the occurrence of diarrhea.

The rule mining identified that mother’s education was a highly significant to predict diarrhea. Accordingly, children whose mothers were not attending formal education were increased the risk of diarrhea compared to those children whose mother were attending formal education. This is similar with conducted studies in Ethiopia⁷⁷ and Nigeria.⁷⁸ This could be due to educated mothers have an awareness regarding the principle of hygiene, feeding and weaning practices, and an educated mothers are more conscious about their children life and keep their children safety as much as possible.

The current study the higher odd of diarrhea was seen among children whose mothers are currently working compared to those children whose mothers do not work currently. This is consistent with studies conducted in Ethiopia and sub-Saharan Africa.^79–81 This might be due to the fact that mothers might not have enough time to provide quality care and implement the principle of hygienic practice for their children.

The present study also showed that wealth index is a noteworthy predictor of diarrhea. Children whose families with a poor wealth index had higher risk of diarrhea compared to children from families with rich wealth index. This finding is congruent with studies done in India,⁸² Sub-Saharan Africa,⁷⁹ and Kenya.⁸³ This might be due to poor wealth index families cannot afford to buy supplies to keep the personal hygiene of their children and they are mostly practiced or use poor sanitation sources and unimproved water source.

This study also stated that the number of living children in the household predicts the diarrheal status of children as it was found that families who had two or more children were more likely to experienced diarrhea than those who had less than two children. This result was consistent with a studies done in Northeast Ethiopia and Western Ethiopia.^12,84 This might be the fact that, the mothers or caregivers might not be proficient to deliver a balanced diet, quality sanitation service, and early medical care when getting sick when the number of children increased. Besides, there might be cross infection when a one siblings getting sick within the same household then the other child might be easily susceptible for diarrhea.

Moreover, the RF classifier identified wasting is an important predictor of diarrhea. This result was consistent with a previous studies in India,⁸⁵ Indonesia,⁸⁶ and Southern Ethiopia.¹³ This could be due to undernutrition reduced the immune system of the child that leads to easily vulnerable for diarrheal diseases.

ML methods play a crucial role in predicting factors that affect population health, including diarrhea among children under the age of 5. This enables informed policy decisions, early detection, targeted prevention strategies, personalized interventions, and efficient resource allocation.^87,88 The study’s practical significance lies in its ability to improve health outcomes by addressing diarrhea’s impact on individuals, families, and the healthcare system in Ethiopia. It introduces innovative perspectives, identifies key risk factors, develops accurate prediction models, and proposes personalized interventions. These contributions provide valuable information for policymakers and program planners, guiding the design of focused interventions to reduce mortality and enhance the health of children under 5 in Ethiopia.

Strength and limitations of the study

The study incorporates five supervised ML classification algorithms used nationwide representative data sets, providing a comprehensive and robust analysis of the predictive capabilities of different algorithms in order to reveals hidden patterns and relationships in the data that may not be easily identifiable through traditional statistical methods. This deepens the understanding of the factors influencing diarrhea among under-5 children in Ethiopia.

Despite its strength, this study has its own limitations. The limitation is that due to limited ML model research, the discussion was done compared with the articles done by using the traditional logistic regression model. Since some attributes (i.e. diarrhea status of the children during the survey) were self-reported, there were chances of recall bias. Moreover, variables like hand washing practice, home-based water treatment, and maternal diarrheal history were not collected and included in the analysis despite it was an important predictor for the outcome variable.

Moreover, the challenges of applying continuous-data methods or ML algorithms to discrete variables are the limitation of our study. Therefore, adapting ML algorithms and developing new methods to handle discrete variables effectively is an active area of research in the field. We acknowledge that our findings may not fully reflect the most up-to-date situation.

Moreover, the findings may not be applicable to other populations or age groups, as our focus was specifically on under-5 children in Ethiopia. Hence, future research should explore diarrhea classification and prediction in diverse demographic groups. Besides, biases or limitations could arise from the feature selection method, only DHS data set used, and limited algorithms included. Therefore, it would be valuable for future research to explore the classification and prediction of diarrhea using many more algorithms, feature selection methods and multiple data sources to address these limitations and to investigate additional areas that can enhance our understanding of diarrhea in this population, ultimately guiding more effective interventions and making strong evidence based decisions.

Implication of the study

Our study can have a significant impact on addressing diarrhea in developing countries. It can enable early detection and diagnosis by analyzing diarrhea-related data, facilitate remote monitoring and telemedicine to overcome healthcare access limitations, optimize treatment strategies based on patient data, aid in public health planning and resource allocation, recommend personalized interventions, and support data-driven research and policy development. However, successful implementation requires addressing challenges such as data availability, healthcare infrastructure, ethical considerations, and model biases. With proper attention to these challenges, the current study can improve the health of children in developing countries.

By utilizing the identified potential factors as indicators, policymakers and healthcare providers can develop interventions that cater to the specific needs of different subgroups in the population. This targeted approach has the potential to improve the health of children and mitigate the impact of diarrhea, particularly in resource-constrained areas. Furthermore, it aligns with the reduction plan of the Millennium Development Goal of under-five mortality.

Here is a more concise draft section on the “Implications of Predicting Diarrhea using ML for Digital Health and M-Health”:

Implications for digital health and M-health

The ability to accurately predict diarrheal episodes using our ML models has important implications for digital health and mobile health (m-health) technologies. Key applications include:

Integration into m-health apps and wearable: Our predictive factors, such as demographics and environmental conditions, can enable personalized risk assessments and early warning alerts to empower users in preventive care and self-management.

Incorporation into clinical decision-support: Digital health platforms can aggregate diverse data sources to generate real-time diarrhea risk profiles, guiding targeted interventions like distribution of oral rehydration solutions or WASH improvements.

Informing population-level strategies: Predictive models can identify high-risk areas to deploy proactive surveillance and tailor public health campaigns for diarrheal disease prevention.

Facilitating continuous learning: M-health integration can enable longitudinal data collection to refine algorithms, monitor trends, and evaluate digital health interventions over time.

In summary, our ML approach holds substantial potential to enhance digital health and m-health solutions, contributing to improved prevention, management, and population health outcomes for diarrheal diseases.

Conclusion

The current study suggested that the RF model outperformed compared with the other ML algorithms based on the performance evaluation metrics. Residence, wealth index, mother’s occupation, educational status, age of child, number of living children in households, drug for intestinal parasite, wasting, and acute respiratory infection were an important predictors of diarrhea among under-five children. Efforts to decrease childhood diarrhea should emphasis on the identified predictors of diarrhea. Moreover, childhood survival can be enhanced noticeably using these identified significant predictors to notify relevant policies and other stakeholders.

The researcher would like to provide the following recommendation based of the current study findings. Future researchers focused on clinical result dataset rather than self- reported data for ML prediction of diarrhea. Future works needs to implement disease prediction application based on the clinically evident symptoms and we recommended for future researcher to access and analyze the most currently available data, in order to present the timeliest information to our readers. Besides, we recommended for other scholars and health informatics professionals to utilize our study as a baseline in order to develop M-health or other digital health that have a significant contribution in the field of digital medicine. Policy makers should be considering this research result and propose diarrhea intervention or controlling mechanism in Ethiopia and the policies should implemented in different area of the country depend on the predictors relationship with diarrhea.

Supplemental Material

Supplemental Material - Comparative analysis of machine learning algorithms for predicting diarrhea among under-five children in Ethiopia: Evidence from 2016 EDHS

Supplemental Material for Comparative analysis of machine learning algorithms for predicting diarrhea among under-five children in Ethiopia: Evidence from 2016 EDHS by Alemu Birara Zemariam, Wondosen Abey, Abdulaziz Kebede Kassaw, and Ali Yimer in Health Informatics Journal.

Footnotes

Acknowledgements

The authors would like to extend their gratitude to the Central Statistical Agency of Ethiopia for the provision of data which helps us to undertake this study.

Authors’ contributions

ABZ and AY contributed to the conception and design, acquisition of data, analysis, and interpretation of data; ABZ, AKK, and WA took part in drafting the article or revising it critically for important intellectual content; all authors agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the research work.

Declaration of conflicting interests

The authors declare that they have no competing of interests with respect to the research, authorship, and publication of this manuscript.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical statement

ORCID iD

Alemu Birara Zemariam

Data availability statement

This study dataset used for the current study was publicly available in the DHS repository () or upon reasonable request from corresponding author by email request via alexb7298@gmail.com.

Supplemental Material

Supplemental material for this article is available online.

Appendix

References

World Health Organization . Diarrheal disease. Available from: https://www.who.int/news-room/fact-sheets/detail/diarrhoeal-disease (2024, accessed on: January 2024).

Maponga

Chirundu

Gombe

, et al. Risk factors for contracting watery diarrhoea in Kadoma City, Zimbabwe, 2011: a case control study. BMC Infect Dis 2013; 13(1): 567–568.

Liu

Johnson

Cousens

, et al. Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. The lancet 2012; 379(9832): 2151–2161.

Diouf

Tabatabai

Rudolph

, et al. Diarrhoea prevalence in children under five years of age in rural Burundi: an assessment of social and behavioural factors at the household level. Glob Health Action 2014; 7(1): 24895.

World Health Organization . Global burden of diarrhea among under five children. Available from: https://www.who.int/news-room/fact-sheets/detail/diarrhoeal-disease (2024, accessed date: 07 June 2024).

Shine

Muhamud

Adanew

, et al. Prevalence and associated factors of diarrhea among under-five children in Debre Berhan town, Ethiopia 2018: a cross sectional study. BMC Infect Dis 2020; 20: 1–6.

Central Statistical Agency [Ethiopia] and ICF . Ethiopia demographic and health survey 2016. Addis Ababa: CSA and ICF, 2016.

Unicef

. Ending preventable child deaths from pneumonia and diarrhoea by 2025: the integrated Global Action Plan for Pneumonia and Diarrhoea (GAPPD). Geneva: WHO & UNICEF, 2013.

Organization

. Pocket book of hospital care for children: guidelines for the management of common childhood illnesses. World Health Organization, 2013.

10.

Workie

Akalu

Baraki

. Environmental factors affecting childhood diarrheal disease among under-five children in Jamma district, South Wello zone, Northeast Ethiopia. BMC Infect Dis 2019; 19: 1–7.

11.

Ferede

. Socio-demographic, environmental and behavioural risk factors of diarrhoea among under-five children in rural Ethiopia: further analysis of the 2016 Ethiopian demographic and health survey. BMC Pediatr 2020; 20(1): 1–9.

12.

Woldu

Bitew

Gizaw

. Socioeconomic factors associated with diarrheal diseases among under-five children of the nomadic population in northeast Ethiopia. Trop Med Health 2016; 44(1): 1–8.

13.

Melese

Paulos

Astawesegn

, et al. Prevalence of diarrheal diseases and associated factors among under-five children in Dale District, Sidama zone, Southern Ethiopia: a cross-sectional study. BMC Publ Health 2019; 19(1): 1–10.

14.

Gedamu

Kumie

Haftu

. Magnitude and associated factors of diarrhea among under five children in Farta Wereda, North West Ethiopia. Qual Prim Care 2017; 25(4): 199–207.

15.

Tesfaye

Atique

Azim

, et al. Predicting skilled delivery service use in Ethiopia: dual application of logistic regression and machine learning algorithms. BMC Med Inf Decis Making 2019; 19(1): 1–10.

16.

Mfateneza

Rutayisire

Biracyaza

, et al. Application of machine learning methods for predicting infant mortality in Rwanda: analysis of Rwanda demographic health survey 2014-15 dataset. BMC Pregnancy Childbirth 2022; 22(1): 388.

17.

Kebede Kassaw

Yimer

Abey

, et al. The application of machine learning approaches to determine the predictors of anemia among under five children in Ethiopia. Sci Rep 2023; 13(1): 22919.

18.

CSA-Ethiopia I. International . Ethiopia demographic and health survey 2016: key indicators report. Rockville: CSA and ICF, 2016.

19.

Demographic DQI, Long TU . DHS methodological reports 30. 2020.

20.

Measure

. The demographic and health surveys program. Macro International, 2013. https://wwwmeasuredhscom/ (Accessed 26 2013).

21.

Croft

Marshall

AMJ

Allen

, et al. Guide to DHS statistics. Rockville, Maryland, USA: ICF, 2018.

22.

Taiwo

Thanni

. Baseline anthropometric measurements and Obesity among students in Sagamu, Ogun State, southwest, Nigeria: baseline anthropometric measurements and Obesity among students. Babcock Univ Med J 2022; 5(2): 103–109.

23.

Stevens

Paciorek

Flores-Urrutia

, et al. National, regional, and global estimates of anaemia by severity in women and children for 2000–19: a pooled analysis of population-representative data. Lancet Global Health 2022; 10(5): e627–e639.

24.

Mugel

Clasen

Bauza

. Global practices, geographic variation, and determinants of child feces disposal in 42 low-and middle-income countries: an analysis of standardized cross-sectional national surveys from 2016–2020. Int J Hyg Environ Health 2022; 245: 114024.

25.

World Health Organization . WHO child growth standards. Available at: https://www.who.int/childgrowth/standards/Technical_report.pdf (2015, accessed on December 2023).

26.

Regassa

Birke

Deboch

, et al. Environmental determinants of diarrhea among under-five children in Nekemte town, western Ethiopia. Ethiopian Journal of Health Sciences 2008; 18(2).

27.

Hamangaba

. Factors associated with diarrhea among under-five children in Zambia. University of Zambia, 2016.

28.

Maniruzzaman

Abedin

. Prediction of childhood diarrhea in Bangladesh using machine learning approach. Insights Biomed Res 2020; 4(1): 111–116.

29.

Abd-Alrazaq

Alalwan

McMillan

, et al. Patients’ adoption of electronic personal health records in England: secondary data analysis. J Med Internet Res 2020; 22(10): e17499.

30.

Langkamp

Lehman

Lemeshow

. Techniques for handling missing data in secondary analyses of large surveys. Acad Pediatr 2010; 10(3): 205–210.

31.

Ratner

. The correlation coefficient: its values range between+ 1/− 1, or do they? J Target Meas Anal Market 2009; 17(2): 139–142.

32.

Luque

Carrasco

Martín

, et al. The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recogn 2019; 91: 216–231.

33.

Setiawan

Serdült

Kryssanov

. A machine learning framework for balancing training sets of sensor sequential data streams. Sensors 2021; 21(20): 6892.

34.

Brownlee

. Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python. Machine Learning Mastery, 2020.

35.

Rudnicki

Wrzesień

Paja

. All relevant feature selection methods and applications. Feature Selection for Data and. Pattern Recogn 2015: 11–28.

36.

Chen

R-C

Dewi

Huang

S-W

, et al. Selecting critical features for data classification based on machine learning methods. J Big Data 2020; 7(1): 52.

37.

Pudjihartono

Fadason

Kempa-Liehr

, et al. A review of feature selection methods for machine learning-based disease risk prediction. Front Bioinform 2022; 2: 927312.

38.

Kursa

Jankowski

Rudnicki

. Boruta–a system for feature selection. Fundam Inf 2010; 101(4): 271–285.

39.

Pedregosa

Varoquaux

Gramfort

, et al. Scikit-learn: machine learning in Python. J Mach Learn Res 2011; 12: 2825–2830.

40.

Ogallo

Speakman

Akinwande

, et al. (eds). Identifying factors associated with neonatal mortality in Sub-Saharan Africa using machine learning. In: AMIA Annual Symposium Proceedings; 2020: American Medical Informatics Association, 2020.

41.

Fenta

Zewotir

Muluneh

. A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones. BMC Med Inf Decis Making 2021; 21(1): 1–12.

42.

Maulana

YDF

Ruldeviyani

Sensuse

(eds). Data mining classification approach to predict the duration of contraceptive use. In: 2020 Fifth International Conference on Informatics and Computing (ICIC). IEEE, 2020.

43.

Chilyabanyama

Chilengi

Simuyandi

, et al. Performance of machine learning classifiers in classifying stunting among under-five children in Zambia. Children 2022; 9(7): 1082.

44.

Zemariam

Yimer

Abebe

, et al. Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia. Sci Rep 2024; 14(1): 9080.

45.

Zemariam

Aligaz

Habesse

, et al. Employing advanced supervised machine learning approaches for predicting micronutrient intake status among children aged 6-23 months in Ethiopia. Front Nutr; 11: 1397399.

46.

Zhang

. Bayesian classification. Fundamentals of image data mining: analysis, features. Classification and Retrieval 2019: 161–178.

47.

Lucy Lawrence

. Predicting stunting status among children under five years: the case study of Tanzania. University of Rwanda, 2021.

48.

Hemo

Rayhan

. Classification tree and random forest model to predict under-five malnutrition in Bangladesh. Biom Biostat Int J 2021; 10(3): 116–123.

49.

Jin

Shang

Zhu

, et al. (eds). RFRSF: employee turnover prediction based on random forests and survival analysis. In: Web Information Systems Engineering–WISE 2020: 21st International Conference, Amsterdam, The Netherlands, October 20–24, 2020, Proceedings, Part II 21. Springer, 2020.

50.

Varoquaux

Colliot

. Evaluating machine learning models and their diagnostic value. Machine Learning for Brain Disorders 2023: 601–630.

51.

Steurer

Hill

Pfeifer

. Metrics for evaluating the performance of machine learning based automated valuation models. J Property Res 2021; 38(2): 99–129.

52.

Hossin

Sulaiman

. A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process 2015; 5(2): 1.

53.

Vujović

. Classification model evaluation metrics. Int J Adv Comput Sci Appl 2021; 12(6): 599–606.

54.

Naidu

Zuva

Sibanda

(eds). A review of evaluation metrics in machine learning algorithms. In: Computer science on-line conference. Springer, 2023.

55.

Goodacre

. On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. J Anal Test 2018; 2(3): 249–262.

56.

Hossain

Timmer

. Machine learning model optimization with hyper parameter tuning approach. Global J Comput Sci Technol 2021; 21(D2): 7–13.

57.

Council

. Frontiers in massive data analysis. Washington, DC: He National Academies Press, 2013.

58.

Datta

George Dalmida

Martinez

, et al. Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in south Florida. Front Digit Health 2023; 5: 1193467.

59.

Roberts

Stewart

Tingley

. Navigating the local modes of big data. Computational social science 2016; 51.

60.

Mangalathu

Hwang

S-H

Jeon

J-S

. Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Eng Struct 2020; 219: 110927.

61.

Prendin

Pavan

Cappon

, et al. The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP. Sci Rep 2023; 13(1): 16865.

62.

Kashifi

. Investigating two-wheelers risk factors for severe crashes using an interpretable machine learning approach and SHAP analysis. IATSS Res 2023; 47(3): 357–371.

63.

Alshankati

Alshibany

Toma

, et al. The use of machine learning models to predict PFS and OS outcomes from waterfall plots in randomized clinical trials (MAP-OUTCOMES). American Society of Clinical Oncology, 2023.

64.

Molnar

. Interpretable machine learning. Lulu. com, 2020.

65.

Zhang

Kang

, et al. Mining association rules between stroke risk factors based on the Apriori algorithm. Technol Health Care 2017; 25(S1): 197–205.

66.

Awotiwon

Pillay‐van Wyk

Dhansay

, et al. Diarrhoea in children under five years of age in South Africa (1997–2014). Trop Med Int Health 2016; 21(9): 1060–1070.

67.

CSA-Ethiopia I . International: Ethiopia demographic and health survey 2011. Central statistical agency of Ethiopia and ICF international addis ababa, Ethiopia and calverton. Maryland, USA: CSA, 2012.

68.

Alebel

Tesema

Temesgen

, et al. Prevalence and determinants of diarrhea among under-five children in Ethiopia: a systematic review and meta-analysis. PLoS One 2018; 13(6): e0199684.

69.

Anteneh

Andargie

Tarekegn

. Prevalence and determinants of acute diarrhea among children younger than five years old in Jabithennan District, Northwest Ethiopia, 2014. BMC Publ Health 2017; 17(1): 1–8.

70.

Zahirzda

Chanmas

Chan

. A data mining model for predicting diarrhea in Afghan children. Authorea Preprints, 2023.

71.

Bin Mohanna

Al-Sonboli

. Prevalence ofdiarrhoea and related risk factors among children aged under 5 years in Sana’a, Yemen. Hamdan Med J 2018; 11: 29–33.

72.

Csa-Ethiopia

. Ethiopia demographic and health survey 2016: key indicators report. Addis Ababa, Ethiopia, and Rockville. Maryland, USA: CSA and ICF, 2016.

73.

Kurisu

Yoshiuchi

Ogino

, et al. Machine learning analysis to identify the association between risk factors and onset of nosocomial diarrhea: a retrospective cohort study. PeerJ 2019; 7: e7969.

74.

Zahirzda

Chanmas

Chan

. A data mining model for predicting diarrhea in afghan children.

75.

Cai

Luo

Wang

, et al. Feature selection in machine learning: a new perspective. Neurocomputing 2018; 300: 70–79.

76.

Dessalegn

Kumie

Tefera

. Predictors of under-five childhood diarrhea: Mecha district, west Gojam, Ethiopia. Ethiop J Health Dev 2011; 25(3): 192–200.

77.

Mohammed

Tilahun

Tamiru

. Morbidity and associated factors of diarrheal diseases among under five children in Arba-Minch district, Southern Ethiopia, 2012. Sci J Publ Health 2013; 1(2): 102–106.

78.

Yilgwan

Okolo

. Prevalence of diarrhea disease and risk factors in Jos university teaching hospital, Nigeria. Ann Afr Med 2012; 11(4): 217–221.

79.

Demissie

Yeshaw

Aleminew

, et al. Diarrhea and associated factors among under five children in sub-Saharan Africa: evidence from demographic and health surveys of 34 sub-Saharan countries. PLoS One 2021; 16(9): e0257522.

80.

Atnafu

Sisay

Demissie

, et al. Geographical disparities and determinants of childhood diarrheal illness in Ethiopia: further analysis of 2016 Ethiopian Demographic and Health Survey. Trop Med Health 2020; 48: 64.

81.

Bado

Susuman

Nebie

. Trends and risk factors for childhood diarrhea in sub-Saharan countries (1990–2013): assessing the neighborhood inequalities. Glob Health Action 2016; 9(1): 30166.

82.

Rahman

. Assessing income-wise household environmental conditions and disease profile in urban areas: study of an Indian city. Epidemiology 2005; 16(5): S43–S44.

83.

Mulatya

Ochieng

. Disease burden and risk factors of diarrhoea in children under five years: evidence from Kenya’s demographic health survey 2014. Int J Infect Dis 2020; 93: 359–366.

84.

Alemayehu

Oljira

Demena

, et al. Prevalence and determinants of diarrheal diseases among under-five children in Horo Guduru Wollega Zone, Oromia Region, Western Ethiopia: a community-based cross-sectional study. Can J Infect Dis Med Microbiol 2021; 2021: 1–9.

85.

Gupta

. Study of the prevalence of diarrhoea in children under the age of five years: it’s association with wasting. Indian J Sci Res 2014; 7(1): 1315–1318.

86.

Agustina

Sari

Satroamidjojo

, et al. Association of food-hygiene practices and diarrhea prevalence among Indonesian young children from low socioeconomic urban areas. BMC Publ Health 2013; 13(1): 1–12.

87.

Ashrafian

Darzi

AJPM

. Transforming health policy through machine learning. PLoS Med 2018; 15(11): e1002692.

88.

Holzinger

Biemann

Pattichis

, et al.

What do we need to build explainable AI systems for the medical domain?

2017.

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