Recursive Feature Elimination and Optimized Hybrid Ensemble Approach for Early Heart Disease Prediction

Abstract

Early machine learning prediction improves patient health and prevents heart disease, one of the leading causes of morbidity worldwide. However, challenges such as noise and incomplete data often obscure patterns critical for accurate predictions, and single-classifier models may fail to capture data complexity. This study aims to develop a robust ensemble model leveraging advanced feature selection techniques to enhance prediction accuracy. Various machine-learning algorithms are examined. Recursive feature elimination is applied to remove irrelevant features, improving model performance. The hybrid ensemble method achieves 93.15% accuracy, 93.15% precision, and 92.97% recall, outperforming Principal Component Analysis and symmetrical uncertainty methods. This research sets a benchmark for future studies by leveraging hyperparameter tuning and advanced feature selection to optimize feature reduction and machine learning models.