A comparative study of ensemble learning based classifiers in heart disease detection.

This study addresses the global challenge of heart disease by emphasizing the need for accurate early detection. It explores and compares multiple ensemble learning algorithms-Gradient Boosting, Random Forest, AdaBoost, XGBoost, Bagging, Extra Trees, Voting, Stacking, HistGradientBoosting, and LightGBM-for heart disease classification. Using comprehensive evaluation metrics including confusion matrix, precision, recall, and F1-score, the research aims to identify the most effective models, enhance diagnostic accuracy, and contribute to the development of more reliable and precise heart disease detection systems.