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

BackgroundCardiovascular disease, especially heart failure, is a substantial global health issue. By integrating PHR with machine learning, early disease detection could be made possible.ObjectiveIn 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.MethodsData 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.ResultsThe 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).ConclusionThese 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.