[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126611-en":3,"doc-seo-126611-105":31,"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126611,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Effective Models for Predicting Heart Disease Using Machine Learning Techniques - A Comparative Study - Article 5","Cardiovascular disease remains a leading cause of death worldwide, and timely clinical assessment is hindered by the complexity and volume of patient information. This study evaluates multiple machine learning methods to predict and classify heart disease, framing the task as a data-driven decision problem within healthcare. Models including KNN, Naïve Bayes, Neural Networks, Decision Trees, SVM, Random Forest, Logistic Regression, Gradient Boosting, SGD, and AdaBoost are trained and compared, and the most accurate approach is selected to improve prediction of heart attack risk with greater efficiency and precision.","Information Sciences Letters  \n\n| Volume 12\u003Cbr>Issue 5 May 2023 | Article 5 |\n| --- | --- |\n| 2023\u003Cbr>Effective Models for Predicting Heart Disease Using Machine Learning Techniques – A Comparative Study\u003Cbr>D. Trabay\u003Cbr>Information System Department, Obour Institutes for Obour High Institute for Management and computers and Information Systems, Kilo 21 Cairo / Belbeis, Obour City, Egypt, [doaatrabay@oi.edu.eg](doaatrabay@oi.edu.eg)\u003Cbr>W. Gharibi\u003Cbr>Division of Computing, Analytics, and Mathematics, School of Computing and Engineering, University of Missouri- Kansas City (UMKC), USA, [doaatrabay@oi.edu.eg](doaatrabay@oi.edu.eg)\u003Cbr>W. M. Abd-Elhafiez\u003Cbr>Computer Science Department, Faculty of Computers and Artificial Intelligence, Sohag University, Sohag, Egypt\\\\ College of Computer Science & Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia, [doaatrabay@oi.edu.eg](doaatrabay@oi.edu.eg)\u003Cbr>Follow this and additional works at: [https://digitalcommons.aaru.edu.jo/isl](https://digitalcommons.aaru.edu.jo/isl) |  |\n\nRecommended Citation  \nTrabay, D.; Gharibi, W.; and M. Abd-Elhafiez, W. (2023) \"Effective Models for Predicting Heart Disease Using Machine Learning Techniques – A Comparative Study,\" Information Sciences Letters: Vol. 12 : Iss. 5 , PP-.  \nAvailable at: [https://digitalcommons.aaru.edu.jo/isl/vol12/iss5/5](https://digitalcommons.aaru.edu.jo/isl/vol12/iss5/5)  \nThis Article is brought to you for free and open access by Arab Journals Platform. It has been accepted for inclusion in Information Sciences Letters by an authorized editor. The journal is hosted on Digital Commons, an Elsevier platform. For more information, please contact [rakan@aaru.edu.jo](rakan@aaru.edu.jo), [marah@aaru.edu.jo](marah@aaru.edu.jo),  \n[u.murad@aaru.edu.jo](u.murad@aaru.edu.jo).  \nInformation Sciences Letters  \nAn International Journal  \n[http://dx.doi.org/10.18576/isl/120505](http://dx.doi.org/10.18576/isl/120505)  \nEffective Models for Predicting Heart Disease Using Machine Learning Techniques – A Comparative Study  \nD. Trabay1,*, W. Gharibi2, and W. M. Abd-Elhafiez3,4  \n1Information System Department, Obour Institutes for Obour High Institute for Management and computers and Information Systems, Kilo 21 Cairo / Belbeis, Obour City, Egypt  \n2Division of Computing, Analytics, and Mathematics, School of Computing and Engineering, University of MissouriKansas City (UMKC), USA  \n3Computer Science Department, Faculty of Computers and Artificial Intelligence, Sohag University, Sohag, Egypt 4College of Computer Science & Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia  \nReceived: 21 Feb. 2023, Revised: 22 Mar. 2023, Accepted: 24 Mar. 2023.  \nPublished online: 1 May 2023 .  \nAbstract: Cardiovascular disease is one of the most important causes of death in the modern world, and a significant barrier to clinical information assessment may be the expectation of cardiovascular illness. Machine learning (ML) has proven helpful for forecasting and decision-making in the healthcare industry's large amount of data. Moreover, ML algorithms have been applied in many important fields such as the internet of things and others.  \nIn this paper, we applied various ML methods to predict and classify heart patients' disease including K-Nearest Neighbor Algorithm (KNN), Naïve Bayes (NB), Neural Network (NN), Decision trees (DT), Support Vector Machine (SVM), Random-Forest (RF), Logistic Regression (LR), Gradient-Boosting GB), Stochastic Gradient Descent (SGD), and Ada-Boost. All models were evaluated, and the most accurate predictive model was chosen to increase the accuracy of heart attack prediction. Compared to other models, our results are efficient, and adequate and could help to predict heart disease more effectively and precisely.  \nKeywords: Algorithms classification, Machine learning techniques, Heart disease, Prediction model.  \n1 Introduction  \nA wide range of illnesses impairing heart function is called \"heart ","cbCaisdctYJrWaaF","https://ap.wps.com/l/cbCaisdctYJrWaaF","pdf",1523346,4,1,13,"English","en",105,"# Abstract\n# Introduction\n## Heart disorders and cardiovascular mortality\n## Early detection and risk factors\n## Machine learning methods for diagnosis\n# Machine Learning Models and Evaluation","[{\"question\":\"Which machine learning algorithms are used for heart disease prediction in this study?\",\"answer\":\"The study applies K-Nearest Neighbor, Naïve Bayes, Neural Network, Decision Trees, Support Vector Machine, Random Forest, Logistic Regression, Gradient-Boosting, Stochastic Gradient Descent, and AdaBoost.\"},{\"question\":\"How does the paper approach the heart disease problem?\",\"answer\":\"It frames heart disease prediction as a classification task, using key features and comparing multiple ML classifiers to forecast and support decision-making.\"},{\"question\":\"What is the purpose of comparing different models?\",\"answer\":\"The comparison identifies the most accurate predictive model, aiming to improve the efficiency and precision of heart attack risk prediction compared with other approaches.\"}]","Effective Models for Predicting Heart Disease Using Machine Learning Techniques - A Comparative Study - Article 5 | PDF",1785933739,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"effective-models-for-predicting-heart-disease-using-machine-learning-techniques-a-comparative-study-article-5","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"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":13,"@type":44,"position":20},"https://docshare.wps.com/document/effective-models-for-predicting-heart-disease-using-machine-learning-techniques-a-comparative-study-article-5/126611/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which machine learning algorithms are used for heart disease prediction in this study?","Question",{"text":76,"@type":77},"The study applies K-Nearest Neighbor, Naïve Bayes, Neural Network, Decision Trees, Support Vector Machine, Random Forest, Logistic Regression, Gradient-Boosting, Stochastic Gradient Descent, and AdaBoost.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the paper approach the heart disease problem?",{"text":81,"@type":77},"It frames heart disease prediction as a classification task, using key features and comparing multiple ML classifiers to forecast and support decision-making.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the purpose of comparing different models?",{"text":85,"@type":77},"The comparison identifies the most accurate predictive model, aiming to improve the efficiency and precision of heart attack risk prediction compared with other approaches.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]