Heart Disease Prediction Using Gradient Boosting, AdaBoost, and XGBoost: Robust Ensemble Learning for Improved Diagnostic Accuracy
Keywords:
Heart Disease Prediction, Ensemble Learning, AdaBoost, Gradient Boosting, XGBoostAbstract
Heart disease remains one of the leading causes of mortality worldwide, emphasizing the need for efficient and accurate diagnostic tools. In this study, we present a comprehensive comparative analysis of three ensemble learning algorithms Gradient Boosting, AdaBoost, and XGBoost—for heart disease prediction. The models are trained and validated using stratified cross-validation, with hyperparameter tuning performed through grid search optimization. Evaluation metrics including Accuracy, Precision, Recall, F1-Score, and ROC-AUC are employed to assess performance consistency and robustness across datasets. Among the three ensemble techniques, the AdaBoost model demonstrates superior predictive accuracy and generalization capability, outperforming both Gradient Boosting and XGBoost. This can be attributed to AdaBoost’s effective handling of misclassified samples by adaptive weight adjustment, thereby improving diagnostic reliability. The findings highlight the potential of ensemble learning frameworks, particularly AdaBoost, in enhancing clinical decision support systems for cardiovascular disease diagnosis. Future research will explore hybrid boosting architectures and deep ensemble integrations to further improve diagnostic precision and computational efficiency.
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