[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125369-en":3,"doc-seo-125369-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},125369,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",7,"Healthcare","Implementation of Machine Learning Algorithm for Heart Attack Disease Prediction","Heart attack disease remains a major global cause of mortality, and manual risk prediction is often unreliable because medical datasets are complex. This study compares five machine learning models—K-Nearest Neighbors, Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machine—using a Kaggle dataset. After preprocessing, data are split into training and testing sets (70:30 and 80:20). Models are evaluated with accuracy, precision, recall, and F1-score, showing Decision Tree and Random Forest reaching up to 97.98%.","Implementation of Machine Learning Algorithm for Heart Attack Disease Prediction  \nFebbi Ardiani1, Irma Fitriani2, Nabil Gustian3 ,  \nMeliani Putri Diamon Chandra4, Hasna Uzakiyah5  \n1,2Department of Information System, Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia  \n3Department of Islamic Studies, Faculty of Adab and Humaniora,  \nSidi Mohamed Ben Abdellah University, Morocco  \n4Departement of Management, Faculty of Economy and Business,  \nKütahya Dumlupınar University, Turkey  \n5Departement of E-commerce, Faculty of Science and Technology,  \nNantong University, China  \n[E-Mail:](E-Mail:112250323179@students.uin-suska.ac.id)[1](E-Mail:112250323179@students.uin-suska.ac.id)[12250323179@students.uin-suska.ac.id](E-Mail:112250323179@students.uin-suska.ac.id), [2](212250325541@students.uin-suska.ac.id)[12250325541@students.uin-suska.ac.id](212250325541@students.uin-suska.ac.id),  \n[3](3gustiannabil@gmail.com)[gustiannabil@gmail.com](3gustiannabil@gmail.com), [4](4melainiputridiamon@gmail.com)[melainiputridiamon@gmail.com](4melainiputridiamon@gmail.com), [5](5hasnauzakiyah21@gmail.com)[hasnauzakiyah21@gmail.com](5hasnauzakiyah21@gmail.com)  \nReceived Jan 01st 2025; Revised Aug 18th 2025; Accepted Aug 30th 2025; Available Online Aug 31th 2025  \nCorresponding Author: Febbi Ardiani  \nCopyright © 2025 by Authors, Published by Institut Riset dan Publikasi Indonesia (IRPI)  \nAbstract  \nHeart attack disease is one of the leading causes of death worldwide, making early detection a critical factor in reducing mortality. However, manual prediction is often inaccurate due to the complexity of medical data. To address this issue, this study evaluates five machine learning algorithms K-Nearest Neighbors (KNN), Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machine (SVM) for predicting heart attack risk. The dataset, obtained from Kaggle, waspreprocessed and divided into training and testing sets using 70:30 and 80:20 ratios. Algorithm performance was assessed using accuracy, precision, recall, and F1-score. The results showed that Decision Tree and Random Forest achieved the best performance with accuracy up to 97.98%, while KNN recorded the lowest accuracy at around 61.36% . This study not only demonstrates the comparative effectiveness of these algorithms on the same dataset, contributing to the growing body of research on AI in healthcare, but also highlights their potential clinical utility. In particular, Decision Tree and Random Forest can support the development of AI-based clinical decision support systems to assist healthcare professionals in early diagnosis and risk management.  \nKeywords: Decision Tree, Heart Attack Prediction, K-Nearest Neighbors, Random Forest, Support Vector Machine  \n1. INTRODUCTION  \nMyocardial infarction, another name for heart attack, occurs when the heart muscle does not receive sufficient blood and oxygen to function properly [1] [2] . Blockage of the arteries that supply blood to the heart is often the main cause, making a heart attack a life-threatening medical emergency [3] [4] . Globally, this disease is one of the leading causes of death, responsible for an estimated 17 million deaths each year. The impact is also evident at the national level: in Pakistan, heart attacks accounted for 19% of total deaths in 2020 and increased to 29% in the following year, while in Indonesia they were recorded as the leading cause of death across all age groups at 12.9%[5] . These figures highlight that heart attacks are not only a global health concern but also a serious national challenge, emphasizing the urgency of research on more accurate early detection methods to help reduce mortality rates.  \nHeart attacks can be caused by various lifestyle factors as well as genetic predisposition [4] . The most common symptoms include chest pain, shortness of breath, and unusual fatigue. In addition, patients may experience dizziness, nausea, excessive sweating, o","cbCaitoDMbdFo0Hu","https://ap.wps.com/l/cbCaitoDMbdFo0Hu","pdf",568663,1,14,"English","en",105,"# Introduction\n## Heart attack background and urgency\n## Symptoms and contributing factors\n## Prevention and treatments\n## Importance of early prediction with AI\n## Prior research on prediction models","[{\"question\":\"Why is early heart attack prediction important?\",\"answer\":\"Early prediction helps save lives by enabling timely diagnosis and risk management, reducing mortality and recurrence risk.\"},{\"question\":\"Which machine learning algorithms are evaluated in the study?\",\"answer\":\"The study evaluates K-Nearest Neighbors, Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machine.\"},{\"question\":\"How is model performance measured?\",\"answer\":\"Performance is assessed using accuracy, precision, recall, and F1-score on training and testing splits.\"}]","Implementation of Machine Learning Algorithm for Heart Attack Disease Prediction | PDF",1785898471,35,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"implementation-of-machine-learning-algorithm-for-heart-attack-disease-prediction","",{"@graph":36,"@context":85},[37,54,68],{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/implementation-of-machine-learning-algorithm-for-heart-attack-disease-prediction/125369/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early heart attack prediction important?","Question",{"text":75,"@type":76},"Early prediction helps save lives by enabling timely diagnosis and risk management, reducing mortality and recurrence risk.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are evaluated in the study?",{"text":80,"@type":76},"The study evaluates K-Nearest Neighbors, Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machine.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance measured?",{"text":84,"@type":76},"Performance is assessed using accuracy, precision, recall, and F1-score on training and testing splits.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]