Objective

Prediagnosis of diseases plays a pivotal role in medical triage. However, only surface-level information is available in this medical service. To achieve the prediagnosis challenge for nontraumatic acute abdomen (NTAA) with limited information, an intelligent framework was proposed.

Methods

This research was conducted using retrospective patients with NTAA data from the Affiliated Hospital of Zunyi Medical University. A machine learning framework, which encompassed a series of combined binary classifiers tailored to various NTAA conditions was developed. Within this framework, disease information was recursively inferred across three tiers: primary categories (I-level), disease subtypes (II-level), and specific diseases (III-level). In model training, the REFCV (Recursive Feature Elimination with Cross-Validation) approach was employed for feature refinement, and five algorithms—Logistic Regression, Deep Neural Networks, Support Vector Machine, Random Forest, and eXtreme Gradient Boosting—were assessed. The data was split into training and testing datasets, with five-fold cross-validation and grid search for model optimization. Performance was evaluated using area under the receiver operating characteristic curve, accuracy, precision, specificity, and sensitivity. The Friedman test and Wilcoxon paired test compared algorithm performance.

Results

I-Level disease identification metrics mostly surpassed 0.90. II-Level classification metrics generally exceeded 0.80. For III-level diseases, models maintained high recognition rates for several common conditions. Logistic regression showed consistent performance comparable to other algorithms.

Conclusion

The framework performed admirably in discerning both primary disease categories and their respective subtypes. The objective of NTAA prediagnosis based solely on superficial information could be realized. Logistic regression proves sufficient for this task, with no significant benefits from more complex algorithms.

Search results

Intelligent prediagnosis for nontraumatic acute abdomen with surface-level information using machine learning