JoWUA

Abstract

The prediction of heart disease with several comorbid conditions (hypertension, diabetes, arrhythmia, and obesity) makes the prediction complex due to nonlinear interactions between conditions and heterogeneous risk patterns. As a solution to these research issues, this paper suggests an Adaptive Convolutional Neural Network (CNN) architecture to predict multi-comorbid heart disease on a unified U.S. Heart Disease dataset (n = 1,025) that is comprised of a union of Cleveland, Hungarian, Switzerland, and VA repositories. Out of the 76 attributes, 24 clinically validated features were then picked up based on correlation ranking, SHAP-based interpretability analysis, and cardiologist validation. These characteristics were converted to structured representations of images through a feature-to-image encoding plan that facilitated learning of patterns that are deep both in space and relationally. In the proposed CNN architecture, layer-driven optimization, regularization through dropout, adaptive learning-rate scheduling, and reproducibility control are included to guarantee consistent generalization in a wide range of comorbidity groups. SHAP-based feature attribution generates clinical and understandable explanations about model predictions, achieving clinical interpretability. There is strong performance on experimental evaluation with 92% accuracy, 89% precision, 90% recall, an F1-score of 0.89, and an ROC-AUC of 0.94, and high sensitivity and specificity that is applicable in a clinical decision-support setting. The proposed framework can provide scalable, interpretable, and clinically reviewed methods of automated multi-comorbid cardiac risk assessment to aid in the integration of AI transparency into healthcare system components and clinical implementation.