Adaptive CNN-Based Multi-Comorbid Heart Disease Prediction Using US Dataset with Clinically Validated Feature Optimization
Year: 2026
Type: article
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.
Authors: Nazia Sultana, Dr.P.K. Kumar
Institution: Visvesvaraya Technological University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W7150856009