Ensemble Learning Approaches for Cardiovascular Disease Prediction
Received: 18 September 2025 | Revised: 4 November 2025 and 20 November 2025 | Accepted: 3 December 2025 | Online: 9 February 2026
Corresponding author: Marshima Mohd Rosli
Abstract
Cardiovascular Disease (CVD) is a primary cause of mortality worldwide, which requires precise and timely predictions to implement effective prevention and intervention strategies. Existing prediction methods, dependent on a single classifier, frequently prove inadequate due to data complexity and class imbalance, leading to incorrect classification. This study aimed to compare the performance of ensemble learning approaches for predicting CVD using clinical datasets. Six ensemble classifiers were evaluated: XGBoost and Gradient Boosting (Boosting), Random Forest and Decision Tree (Bagging), and K-Nearest-Neighbor and Logistic Regression (Stacking). Boosting classifiers achieved the highest accuracy (73.0%) and G-mean (72.0%) for CVD prediction, while the bagging classifier for Random Forest achieved similar precision in identifying high-risk patients with CVD. Boosting classifiers demonstrate strong discriminative power in differentiating between risk, as confirmed by the ROC AUC (0.80), indicating their effectiveness for reliable CVD prediction.
Keywords:
component, cardiovascular, ensemble learning, prediction, classifiersDownloads
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Copyright (c) 2025 Ahmad Muzammil Halim, Marshima Mohd Rosli, Norzilah Musa, Nurain Ibrahim
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