[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126389-en":3,"doc-seo-126389-105":30,"detail-sidebar-cat-0-en-105":92},{"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":11,"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":14,"update_tm":28,"read_time":29},126389,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","A Hybrid Extreme Machine Learning Model for Predicting Heart Disease","A hybrid extreme machine learning model (HEMLM) is presented to support early and rapid heart disease diagnosis and to help less experienced physicians interpret clinical heart disease data. The approach combines multi-layer perceptron (MLP), random layers, and logistic regression (LR), enabling multiple feature-pattern views and classification strategies. Experiments compare HEMLM with SVM, LR, and Naive Bayes, showing higher accuracy, including 94.91% under an 85:15 split and strong performance across other splitting ratios, while adding data visualization to clarify feature relationships.","A hybrid extreme machine learning model for predicting heart  \ndisease  \nAbdelmoty M. Ahmed1, Bilal Bataineh2, Ghazi Shakah1, Marwa O. Al Enany3, Maie M. Aboghazalah4,  \nMahmoud M. Khattab5  \n1Department of Computer Science, Faculty of Information and Technology, Ajloun National University, Ajloun, Jordan 2Department of Computer Science, Jadara University, Irbid, Jordan  \n3Higher Institute of Computer Science and Information Systems, Giza, Egypt  \n4Computer Science Collage, Nahda University in Beni Suef, Beni Suef, Egypt 5Department of Computer Science, International Islamic University Malaysia, Kuala Lumpur, Malaysia  \nArticle history:  \nReceived Feb 5, 2025 Revised Jul 21, 2025 Accepted Sep 1, 2025  \nKeywords:  \nClassification algorithms Heart disease  \nHybrid extreme machine learning model algorithm Machine learning Multi-layer perceptron Prediction model Visualization  \nCorresponding Author:  \nHeart disease (HD), the leading cause of death for adults over 65, can affect anyone at any time. Additionally, modern lifestyles, poor diets, and other factors have led to an increased risk of HD among teenagers. One significant challenge is managing and analysing vast amounts of data, often surpassing terabytes, which is crucial for researching, diagnosing, and predicting cardiovascular diseases quickly. To enhance primary health care, especially in early and rapid diagnosis of heart attacks and to assist less experienced doctors in understanding clinical HD data, we propose a hybrid method called the \"hybrid extreme machine learning model (HEMLM)\". This technique combines the strengths of multi-layer perceptron (MLP), random layers, and logistic regression (LR) . The model offers various feature patterns and multiple classification techniques. Compared to support vector machine (SVM), LR, and Naive Bayes (NB), the HEMLM algorithm demonstrates superior performance and efficiency. Testing results show identification accuracies of 94.91%, 94.77%, 92.42%, and 87. 14% for data splitting ratios of 85:15, 80:20, 70:30, and 60:40, respectively.  \nThis is an open access article under the CC BY-SA license.  \nAbdelmoty M. Ahmed  \nDepartment of Computer Science, Faculty of Information and Technology, Ajloun National University Ajloun, Jordan  \nEmail: [abd2005moty@yahoo.com](abd2005moty@yahoo.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nHeart disease (HD) remains one of the leading causes of mortality in today's competitive world. Accurate prognosis of HD is essential for analyzing clinical data, as heart-related issues are the primary contributors to illness and death. Identifying HD based on risk factors like diabetes, high blood pressure, high cholesterol, abnormal heart rate, and others can be challenging. The increasing number of individuals affected by these conditions, combined with unhealthy habits like smoking and excessive drinking, raises the risk of cardiovascular disease [1], [2] . Therefore, timely diagnosis is crucial for protecting affected patients. Additionally, lifestyle and dietary habits significantly impact individual health, making it vital to analyze patient history for early prediction and diagnosis of HD.  \nLearning algorithms offer valuable insights by making predictions from the vast amount of medical data available. Machine learning (ML) techniques have been increasingly used in conjunction with advancements in the internet of things (IoT) . Many studies have utilized ML to provide concise predictions of HD. Techniques like data mining (DM) and neural networks (NN) have been widely applied to understand  \nthe significance of HD among individuals [3], [4] . Various methods, such as k-nearest neighbor (KNN), decision trees (DT), genetic algorithms (GA), and Naive Bayes (NB) have been employed to categorize the severity of HD. However, HD is a complex condition that must be managed carefully; failure to do so can lead to severe consequences, including death [5], [6] . Clinical science and DM concepts are utilized ","cbCairxWsLAwqMJU","https://ap.wps.com/l/cbCairxWsLAwqMJU","pdf",762368,1,13,"English","en",105,"# Introduction\n## Literature Review","[{\"question\":\"What problem does the hybrid extreme machine learning model (HEMLM) target?\",\"answer\":\"HEMLM targets early and rapid prediction/diagnosis of heart disease using clinical data, aiming to assist doctors in understanding risk-related information.\"},{\"question\":\"How does HEMLM combine different learning methods?\",\"answer\":\"HEMLM integrates multi-layer perceptron (MLP), random layers, and logistic regression (LR), then tests projection with different feature patterns and classification techniques.\"},{\"question\":\"How does HEMLM perform compared with other classifiers?\",\"answer\":\"The results show superior accuracy and efficiency versus SVM, LR, and Naive Bayes, with reported accuracies up to 94.91% for a specific data split.\"}]","A Hybrid Extreme Machine Learning Model for Predicting Heart Disease | PDF",1785904797,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-hybrid-extreme-machine-learning-model-for-predicting-heart-disease","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-hybrid-extreme-machine-learning-model-for-predicting-heart-disease/126389/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the hybrid extreme machine learning model (HEMLM) target?","Question",{"text":76,"@type":77},"HEMLM targets early and rapid prediction/diagnosis of heart disease using clinical data, aiming to assist doctors in understanding risk-related information.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does HEMLM combine different learning methods?",{"text":81,"@type":77},"HEMLM integrates multi-layer perceptron (MLP), random layers, and logistic regression (LR), then tests projection with different feature patterns and classification techniques.",{"name":83,"@type":74,"acceptedAnswer":84},"How does HEMLM perform compared with other classifiers?",{"text":85,"@type":77},"The results show superior accuracy and efficiency versus SVM, LR, and Naive Bayes, with reported accuracies up to 94.91% for a specific data split.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]