A Robust Hybrid Ensemble Approach for Cardiovascular Disease Prediction with Feature Optimization - Payel Sengupta; Debmalya Mukherjee; RANJAN BANERJEE; AMARTYA GHOSH; PRANAB GHARAI; RIA PYNE; Piyali De; Avijit Kumar Chaudhuri; Sanjib Bayen (The Bioscan, 2026)
Cardiovascular diseases (CVDs) continue to be the world's leading cause of death, accounting for 17.9 million deaths annually. For effective prevention and intervention, accurate early prediction systems are crucial. The inability of conventional statistical risk models to capture the complex, nonlinear interactions among clinical risk factors frequently limits their predictive accuracy. To address these problems, this study evaluates optimized machine learning methods for cardiovascular disease prediction using the Framingham Heart Study dataset, a reliable longitudinal clinical resource. A comprehensive preprocessing pipeline that included clinically guided feature selection, the removal of incomplete records, feature standardization, and class imbalance correction using Borderline-SMOTE2 was implemented to enhance minority class detection. Four extreme-tuned classifiers—Random Forest, AdaBoost, Support Vector Machine (SVM), and Decision Tree—were systematically trained and compared using a number of evaluation metrics, including accuracy, ROC-AUC, sensitivity, specificity, F1-score, and Cohen's Kappa. Experimental results demonstrate that the optimized AdaBoost and Random Forest models achieved the best predictive performance, with accuracies of 94.12%. AdaBoost had the best overall discrimination, with an ROC-AUC of 0.9775, F1-score of 0.9406, and Cohen's Kappa of 0.8824. With a ROC-AUC of 0.9764, F1-score of 0.9402, and Kappa of 0.8823, Random Forest ranked second. SVM provided strong sensitivity (0.9419) but lower specificity, while the Decision Tree performed comparatively poorly. These findings confirm that ensemble-based methods provide improved stability, balanced classification, and better generalization for cardiovascular risk prediction. The proposed framework offers a clinically reliable and understandable decision- support solution for early detection of cardiovascular disease.