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

Jitendra P Chaudhari; Kishan P Patel; Hiren K Mewada; Hardikkumar Sudhirbhai Jayswal; Yogesh P Kosta; Kanchan S Bhagat; Shubhangi D Kirange

doi:10.46604/aiti.2024.13825

Authors

Jitendra P Chaudhari Charusat Space Research and Technology Center; Department of Electronics and Communication Engineering; Chandubhai S Patel Institute of Technology; Charotar University of Science and Technology, Changa, Gujarat, India
Kishan P Patel Department of Electrical Engineering; Chandubhai S Patel Institute of Technology, Charotar University of Science and Technology, Changa, Gujarat, India
Hiren K Mewada Electrical Engineering Department, Prince Mohammad Bin Fahd University, Al Khobar, Kingdom of Saudi Arabia
Hardikkumar Sudhirbhai Jayswal Department of Information Technology, Devang Patel Institute of Advance Technology and Research, Gujarat, India
Yogesh P Kosta Provost, UKA Tarsadia University , Gujarat, India
Kanchan S Bhagat Dept of Computer Engineering, J. T. Mahajan College of Engineering, Jalgaon, Maharashtra, India
Shubhangi D Kirange Department of Information Technology, Government Polytechnic, Jalgaon, Maharashtra, India

DOI:

https://doi.org/10.46604/aiti.2024.13825

Keywords:

ensemble machine learning, heart disease, hyperparameter tuning, recursive feature elimination

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.

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