[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118586-en":3,"doc-seo-118586-105":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":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":14,"update_tm":28,"read_time":29},118586,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","COMPARISON OF SUPERVISED MACHINE LEARNING ALGORITHMS IN HEART FAILURE DISEASE","Heart failure requires timely diagnosis and treatment because delays can lead to severe outcomes and death. This research builds a supervised machine learning classification approach to predict a patient’s likelihood of heart failure and support earlier, more accurate clinical decision-making. Models evaluated include decision trees, random forests, Naïve Bayes, SVM, and K-NN. Results show Random Forest achieves the highest accuracy at 74.35%, while SVM reaches 69.23% and Naïve Bayes records the lowest at 51.28%, making Random Forest the recommended method.","COMPARISON OF SUPERVISED MACHINE LEARNING ALGORITHMS IN HEART FAILURE DISEASE  \nJauhara Rana Budiani1*, Nur Mahmudah2  \n1,2Study Program of Statistics, Faculty of Sciences and Technology, Universitas Nahdlatul Ulama Sunan Giri Jln. JenderalAhmad Yani, No. 10, Bojonegoro 62115, Indonesia  \nCorresponding author’s e-mail: *[jbudiani@unugiri.ac.id](jbudiani@unugiri.ac.id)  \nArticle History:  \nReceived: 20th February 2025  \nRevised: 15th April 2025  \nAccepted: 23rd May 2025  \nAvailable online: 1st September 2025  \nKeywords:  \nClassification; Disease prediction;  \nHeart Failure;  \nMachine Learning.  \nThe heart is a vital organ in the human body that functions to pump blood throughout the body and to the lungs. The heart is located in the chest cavity. The heart is the main force that drives human life. Therefore, if there is a disturbance in heart function, this can cause a decrease in quality of life to death, one of which is heart failure. Heart failure, if not diagnosed and treated quickly, will result in death. Based on findings showing the high death rate due to heart failure, a classification is needed to predict heart failure using machine learning methods. Machine learning can help predict this disease to improve early detection and more accurate medical decision-making. This study focuses on predicting the likelihood of a patient experiencing heart failure. The machine learning algorithm method used is supervised machine learning classification, including decision trees, random forests, naïve bayes, SVM, and K-NN. The results showed that the best method for predicting heart failure was Random Forest with an accuracy of 74.35%, followed by SVM with an accuracy of 69.23%. Meanwhile, Naïve Bayes had the lowest accuracy of 51.28%. Based on these findings, Random Forest is recommended as the best method for heart failure prediction due to its ability to handle data complexity and provide more stable results. Once the best algorithm is obtained, the prediction results and early detection of heart failure will be more accurate.  \nThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-ShareAlike 4.0 International License ([https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/)) .  \nHow to cite this article:  \nJ. R. Budiani and N. Mahmudah, “COMPARISON OF SUPERVISED MACHINE LEARNING ALGORITHMS IN HEART FAILURE DISEASE”, BAREKENG: J. Math. & App., vol. 19, iss. 4, pp. 2739-2750, December, 2025.  \nCopyright © 2025 Author(s)  \nJournal homepage: [https://ojs3.unpatti.ac.id/index.php/barekeng/](https://ojs3.unpatti.ac.id/index.php/barekeng/)  \nJournal e-mail: [barekeng.math@yahoo.com](barekeng.math@yahoo.com); [barekeng.journal@mail.unpatti.ac.id](barekeng.journal@mail.unpatti.ac.id)  \nResearch Article ∙ Open Access  \n1. INTRODUCTION  \nThe heart is an essential organ in the human body, responsible for circulating blood to the lungs and throughout the body. Positioned within the chest cavity, it acts as the primary force sustaining human life [1] . So that, if there is a problem with heart function, it can lead to a decreased quality of life and even death, one of which is due to heart failure. Heart failure is a disease with the potential to cause death. With advancesin technology, machine learning algorithms provide an optimal solution for predicting heart disease. Thus, these algorithms are important for studies aimed at reducing the mortality rate of heart failure by earlier prediction classification [2] .  \nOne of the primary causes of death globally is heart disease, responsible for 16% of all fatalities. The death has increased from 2.7 million in 2000 to 9.1 million in 2021 [3] . Using data from the Global Burden of Disease and the Institute for Health Metrics and Evaluation (IHME) between 2014 and 2019, the leading cause of death in Indonesia is heart disease. According to the Basic Health Research Data (Riskesdas) from 2013 ","cbCairxIPvEqDZZx","https://ap.wps.com/l/cbCairxIPvEqDZZx","pdf",692939,1,12,"English","en",105,"# Introduction\n## Motivation for early heart failure prediction\n## Need for comparative machine learning methods\n# Study Focus and Methods\n## Supervised classification algorithms evaluated\n# Results and Recommendations\n## Best-performing model selection","[{\"question\":\"Why is early detection of heart failure important?\",\"answer\":\"Early detection helps prevent more serious complications and reduces mortality. It improves the chance of timely diagnosis and intervention.\"},{\"question\":\"Which supervised machine learning algorithms were compared in the study?\",\"answer\":\"The study compares decision trees, random forests, Naïve Bayes, SVM, and K-NN for heart failure prediction.\"},{\"question\":\"What is the best-performing algorithm and its accuracy?\",\"answer\":\"Random Forest is the best method, with an accuracy of 74.35%. SVM follows at 69.23%, and Naïve Bayes is lowest at 51.28%.\"}]","COMPARISON OF SUPERVISED MACHINE LEARNING ALGORITHMS IN HEART FAILURE DISEASE | PDF",1785684392,30,{"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},"comparison-of-supervised-machine-learning-algorithms-in-heart-failure-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/comparison-of-supervised-machine-learning-algorithms-in-heart-failure-disease/118586/",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","2026-08-05","2026-08-02",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},"Why is early detection of heart failure important?","Question",{"text":76,"@type":77},"Early detection helps prevent more serious complications and reduces mortality. It improves the chance of timely diagnosis and intervention.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which supervised machine learning algorithms were compared in the study?",{"text":81,"@type":77},"The study compares decision trees, random forests, Naïve Bayes, SVM, and K-NN for heart failure prediction.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the best-performing algorithm and its accuracy?",{"text":85,"@type":77},"Random Forest is the best method, with an accuracy of 74.35%. 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