Ensemble Based Machine Learning Approach for Heart Disease Prediction

Ensemble machine learning has developed into a strong approach for enhancing the precision and resilience of predictive models through the integration of various learning algorithms. This research presents an innovative ensemble classification framework employing a soft voting approach that combines three gradient boosting techniques XGBoost, LightGBM, and CatBoost to improve heart disease prediction efficacy. The model undergoes evaluation using four distinct datasets (Heart Attack Risk Prediction Dataset, Heart Attack Dataset, Cleveland Heart Disease dataset and Heart Disease Dataset) obtained from Kaggle and other repositories, each reflecting various populations and diagnostic variables. By implementing thorough preprocessing, careful feature selection, and even training-testing-validating splits, the system attains reliable and exceptional classification performance. Experimental findings reveal that the suggested ensemble approach greatly surpasses classic and standalone models, attaining flawless or nearly flawless accuracy on all datasets, reaching a peak accuracy of 100% on the first dataset, 98% on the second dataset, 100% on the third dataset and 98.4% on the fourth dataset. The framework's achievement underscores its viability for real world use in clinical decision support systems and emphasizes the efficiency of ensemble methods in medical diagnosis.