Leveraging personal health records for early heart failure risk prediction through AI-driven modeling

Abstract

Background

Cardiovascular disease, especially heart failure, is a substantial global health issue. By integrating PHR with machine learning, early disease detection could be made possible.

Objective

In this study, an attempt was made to develop and fine-tune an AI model that would forecast the likelihood of heart failure based on patient data in PHRs.

Methods

Data from 1025 patients and 12 clinical/demographic criteria were used. An untuned multilayer perceptron (MLP) with two hidden layers (10 and 5 neurons) was first trained (1000 epochs). Then, using the same dataset, we performed systematic hyperparameter tuning (grid search with 5–fold cross–validation) for Logistic Regression, Random Forest, SVM, and an enhanced MLP. Performance metrics included accuracy, precision, recall, F1–score, MCC, and ROC–AUC with 95% confidence intervals.

Results

The original untuned MLP gave a mean accuracy of 0.7244 (±0.0245) and mean ROC–AUC of 0.724 (±0.038). After tuning, Random Forest achieved the highest performance (AUC = 0.959, 95% CI 0.924–0.986; accuracy = 0.890). The tuned MLP reached AUC = 0.830 (CI 0.763–0.893), outperforming the untuned version and showing comparable performance to Logistic Regression (AUC = 0.824) and SVM (AUC = 0.842).

Conclusion

These results suggest potential use of AI models to anticipate the risk of heart failure from a subject's medical history and provide an avenue toward scalable and personal medicine, resulting in improved early prevention and treatment of cardiovascular disease.