Volume 16, Number 3
OWE-CVD: An Optimized Weighted Ensemble for Heart Disease Prediction
Authors
Fahad Almalki and Abdullah A Sheikh, Taif University, Kingdom of Saudi Arabia
Abstract
Cardiovascular disease (CVD) remains the leading cause of death globally, with roughly 17.9 million fatalities each year. Early and accurate diagnosis of heart disease is critical to improving patient outcomes. We propose OWE-CVD (Optimized Weighted Ensemble for Cardiovascular Disease), a new predictive framework that combines a weighted voting ensemble of three gradient boosting classifiers (XGBoost, LightGBM, and CatBoost) with explainable AI (XAI) techniques. We first addressed class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE) and optimized each model’s hyperparameters using the Optuna framework with stratified 10-fold cross-validation. Ensemble weights were derived from cross-validated accuracy scores. On an independent test set, OWE-CVD achieved 94.44% accuracy with balanced precision, recall, and F1-scores across both classes. We applied SHAP and LIME to interpret the ensemble’s predictions at global and local levels. Overall, OWE-CVD demonstrates strong predictive performance while providing transparent decision support for heart disease diagnosis in clinical settings.
Keywords
Heart Disease Prediction, OWE-CVD, Optimized Weighted Ensemble, Explainable AI (XAI), Machine Learning, Healthcare AI