Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Khalaf Allah, Khaled A. A.
Department of Mathematics, Faculty of Science, Al-Azhar University
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Ensemble Based Machine Learning Approach for Heart Disease Prediction El Shenbary, H. A.; Hassan, Belal Z.; Elsayed, Amr T. A.; Khalaf Allah, Khaled A. A.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.7015

Abstract

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.
Co-Authors El Shenbary, H. A. Elsayed, Amr T. A. Hassan, Belal Z.