Hyperparameter Optimization of Ensemble Learning for Heart Disease Prediction using Patient Data

Abstract

This study evaluates the impact of hyperparameter optimization on the performance of four machine learning algorithms—Extra Trees, XGBoost, Random Forest, and AdaBoost—in heart disease prediction. The results show that hyperparameter tuning significantly improves model performance for three out of the four algorithms, with varying effects across models. Extra Trees demonstrates the most consistent improvement, achieving the highest Area Under the Curve (AUC) of 0.9107 and a recall of 80.93%, which is particularly crucial in medical contexts for accurately identifying disease cases. XGBoost exhibits the largest increase in accuracy, rising from 78.11% to 81.49%, while Random Forest shows improvements in both recall and F1-score. In contrast, AdaBoost experiences a slight decline in performance, suggesting that the model was already near optimal prior to tuning. Overall, Extra Trees with hyperparameter optimization emerges as the best-performing algorithm for heart disease prediction, offering high reliability in identifying at-risk patients.

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