[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125392-en":3,"doc-seo-125392-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":28,"update_tm":29,"read_time":30},125392,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Ensemble Based Machine Learning Approach for Heart Disease Prediction ","\u003Cp>Ensemble machine learning is used to enhance the precision and robustness of predictive models through combining multiple learning algorithms. This study proposes an ensemble classification framework with a soft-voting strategy integrating XGBoost, LightGBM, and CatBoost to improve heart disease prediction. Four datasets from Kaggle and other repositories are evaluated using preprocessing, feature selection, and careful train-test-validation splitting to ensure dependable performance. Experimental results outperform classic and standalone models, reaching peak accuracy up to 100% and near-perfect scores on all datasets, supporting clinical decision support use.\u003C/p>","\u003Cp>Indonesian Journal of Electrical Engineering and Informatics (IJEEI)  \u003C/p>\u003Cp>Vol. 13, No. 3, September 2025, pp. 731∼749  \u003C/p>\u003Cp>ISSN: 2089-3272, DOI: 10.52549/ijeei.v13i3.7015 ❒ 731  \u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003Cp>| Ensemble Based Machine Learning Approach for Heart\u003Cbr>Disease Prediction\u003Cbr>H. A. El Shenbary1 , Belal Z. Hassan2 , Amr T. A. Elsayed3 , Khaled A. A. Khalaf Allah4\u003Cbr>1,2,3,4Department of Mathematics, Faculty of Science, Al-Azhar University, Nasr-City, Cairo, Egypt. |  |  |\u003C/p>\u003Cp>| --- | --- | --- |\u003C/p>\u003Cp>| Article Info\u003Cbr>Article history:\u003Cbr>Received Jul 24, 2025 Revised Sep 19, 2025 Accepted Sep 27, 2025\u003Cbr>Keywords:\u003Cbr>Heart Disease\u003Cbr>Ensemble Machine Learning Classification\u003Cbr>Artificial Intelligence Prediction | ABSTRACT\u003Cbr>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.\u003Cbr>Copyright © 2025 Institute of Advanced Engineering and Science.\u003Cbr>All rights reserved. |  |\u003C/p>\u003Cp>| Corresponding Author: |  |  |\u003C/p>\u003Cp>| Hassan Ahmed El Shenbary\u003Cbr>Department of Mathematics, Faculty of Science, Al-Azhar University. Nasr-City, Cairo, Egypt.\u003Cbr>Email: [h.a.elshenbary@azhar.edu.eg](h.a.elshenbary@azhar.edu.eg) |  |  |\u003C/p>\u003Cp>| 1. INTRODUCTION\u003Cbr>The significant progress in computer science and its effective implementation in various fields have turned computers into far more than simple calculating devices like optimization problems and ubiquitous computing. This progression has greatly inspired researchers and scientists to create cutting edge technologies that utilize computer capabilities to undertake significant tasks and address real world issues, ultimately improving human existence and reducing everyday obstacles. Included among these emerging technologies are expert systems, computer networks, and different kinds of classification algorithms [1] .\u003Cbr>A key aim of Artificial Intelligence (AI) research is the identification of diseases, particularly in the healthcare sector [2] . AI seeks to assist healthcare providers physicians, medical facilities, and institutions by offering diagnostic tools that enhance decision making precision and minimize errors arising from inexperience or high pressure situations. These systems provide quick access to extensive medical information regarding patient tests, facilitating more efficient and informed diagnoses. Heart attacks are among the most dangerous medical conditions. According to the World Health Organization (WHO), heart disease causes roughly 12 million fatalities each year [3] . Given the gravity of this situation, computer scientists have historically con- |  |  |\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003Cp>Journal homepage: [https://section.iaesonline.com/index.php/IJEEI](https://section.iaesonline.com/index.php/IJEEI)  \u003C/p>\u003Cp>centrated on creating diagnostic tools to supp\u003C/p>","cbCaivjHtc7P6KtC","https://ap.wps.com/l/cbCaivjHtc7P6KtC","pdf",891123,1,19,"English","en",105,"# Introduction\n# Related Work and Motivation\n# Proposed Ensemble Framework\n## Soft Voting with XGBoost LightGBM CatBoost\n# Datasets and Experimental Setup\n## Preprocessing and Feature Selection\n# Results and Discussion\n# Conclusion","[{\"question\":\"What ensemble method is proposed for heart disease prediction?\",\"answer\":\"The framework uses soft voting to combine three gradient boosting models: XGBoost, LightGBM, and CatBoost for ensemble classification.\"},{\"question\":\"Which datasets are used to evaluate the model?\",\"answer\":\"Four datasets are used: Heart Attack Risk Prediction Dataset, Heart Attack Dataset, Cleveland Heart Disease dataset, and Heart Disease Dataset, sourced from Kaggle and other repositories.\"},{\"question\":\"How does the proposed approach perform compared with standalone models?\",\"answer\":\"Experimental findings show the ensemble approach surpasses classic and standalone models, achieving up to 100% accuracy on one dataset and near-perfect results on the others.\"}]","Ensemble Based Machine Learning Approach for Heart Disease Prediction  | PDF","Ensemble machine learning is used to enhance the precision and robustness of predictive models through combining multiple learning algorithms. This study proposes an ensemble classification framework with a soft-voting strategy integrating XGBoost, LightGBM, and CatBoost to improve heart disease prediction. Four datasets from Kaggle and other repositories are evaluated using preprocessing, feature selection, and careful train-test-validation splitting to ensure dependable performance. Experimental results outperform classic and standalone models, reaching peak accuracy up to 100% and near-perfect scores on all datasets, supporting clinical decision support use.",1785899125,48,{"code":4,"msg":32,"data":33},"ok",{"site_id":24,"language":23,"slug":34,"title":13,"keywords":35,"description":28,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"ensemble-based-machine-learning-approach-for-heart-disease-prediction-volume-13-issue-3","",{"@graph":37,"@context":87},[38,56,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":54,"@type":44,"position":55},"https://docshare.wps.com/document/ensemble-based-machine-learning-approach-for-heart-disease-prediction-volume-13-issue-3/125392/","Ensemble Based Machine Learning Approach for Heart Disease Prediction",4,{"url":53,"name":13,"@type":57,"author":58,"headline":13,"publisher":60,"fileFormat":63,"inLanguage":23,"description":28,"dateModified":64,"datePublished":64,"encodingFormat":63,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":59},"Person",{"url":42,"name":61,"@type":62},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What ensemble method is proposed for heart disease prediction?","Question",{"text":77,"@type":78},"The framework uses soft voting to combine three gradient boosting models: XGBoost, LightGBM, and CatBoost for ensemble classification.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which datasets are used to evaluate the model?",{"text":82,"@type":78},"Four datasets are used: Heart Attack Risk Prediction Dataset, Heart Attack Dataset, Cleveland Heart Disease dataset, and Heart Disease Dataset, sourced from Kaggle and other repositories.",{"name":84,"@type":75,"acceptedAnswer":85},"How does the proposed approach perform compared with standalone models?",{"text":86,"@type":78},"Experimental findings show the ensemble approach surpasses classic and standalone models, achieving up to 100% accuracy on one dataset and near-perfect results on the others.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":61,"og:description":28},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,122,125,130,133,137],{"id":20,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":55,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":120,"slug":121},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":123,"slug":124},30,"research-report",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":128,"slug":129},9,"Religion & Spirituality",20,"religion-spirituality",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":128,"slug":132},"World Cup","world-cup",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":134,"slug":136},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},"General","general"]