[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117011-en":3,"doc-seo-117011-105":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},117011,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Approaches for Heart Disease Detection - A Comprehensive Review","This paper presents a comprehensive review of machine learning algorithms for early detection of heart disease, addressing a leading global health concern that requires efficient and accurate diagnostic support. It explains core machine learning fundamentals, summarizes prevalent algorithms used in heart disease detection, and reviews key research efforts applying ML to diagnosis. A detailed tabular comparison highlights relative strengths and weaknesses of algorithms and methodologies, emphasizing progress while identifying the need for further work to improve clinical applicability and efficacy.","Machine Learning Approaches for Heart Disease Detection:A Comprehensive Review  \nHananA. Taher1*, Adnan M. Abdulazeez2  \n1Information Technology Department, Duhok Technical College, Polytechnic University, Iraq  \n2Technical College of Engineering, Duhok Technical College, Polytechnic University, Iraq Email:* [hanan.taher@dpu.edu.com](hanan.taher@dpu.edu.com)  \nAbstract. This paper presents a comprehensive review of the application of machine learning algorithms in the early detection of heart disease. Heart disease remains a leading global health concern, necessitating efficient and accurate diagnostic methods. Machine learning has emerged as a promising approach, offering the potential to enhance diagnostic accuracy and reduce the time required for assessments. This review begins by elucidating the fundamentals of machine learning and provides concise explanations of the most prevalent algorithms employed in heart disease detection. It subsequently examines noteworthy research efforts that have harnessed machine learning techniques for heart disease diagnosis. A detailed tabular comparison of these studies is also presented, highlighting the strengths and weaknesses of various algorithms and methodologies. This survey underscores the significant strides made in leveraging machine learning for early heart disease detection and emphasizes the ongoing need for further research to enhance its clinical applicability and efficacy.  \nKeywords: Machine Learning, Classification Techniques, Supervised Learning, Na ïve Bayes, Support Vector Machine, Heart Disease, Decision Trees, K-Nearest Neighbor, Random Forest  \nARTICLE INFO:  \nSubmitted/Received 1 May 2023  \nFirst revised 12 Jul 2023  \nAccepted 18 Oct 2023  \nFirst available online 08 Dec 2023  \nPublication date 25 Dec 2023  \n1. INTRODUCTION  \nCardiovasculardiseases (CVDs) persist asthe foremost cause of mortality globally, exactinga profound toll on public health and healthcare systems worldwide [1]. Among the diverse spectrum of CVDs, heart disease, which encompasses conditions such as coronary artery disease, heart failure, and arrhythmias, remains a particularly formidable adversary [2] . The imperative for early detection and intervention in heart disease cannot be overstated, as it directly impacts patient outcomes, healthcare costs, and societal well-being [3] .  \nMachine learning, an advanced segment of artificial intelligence, has shown remarkable capabilities in various fields, including the assessment of facial attractiveness and disease prediction. In facial attractiveness prediction, machine learning algorithms are trained using vast datasets of facial images, each rated for attractiveness by human observers [4] . These algorithms then learn to identify patterns and features that correlate with perceived attractiveness [5]. Deep Neural Networks is used for the task of facial attractiveness assessment by applying knowledge gained from one domain and applies it to a related but different domain [6, 7] . This approach is particularly beneficial in scenarios with limited data. This technology not only aids in understanding human perceptions of beauty but also finds applications in cosmetic surgery, advertising, and social media filters. In the realm of disease prediction, machine learning analyzes vast medical data to predict diseases, like early breast cancer detection [8] or echocardiograms for heart disease prediction.  \nIn recent years, the convergence of healthcare data proliferation, computational prowess, and machine learning (ML) methodologies has reshaped the landscape of heart disease detection [9]. The utility of ML algorithms, driven by their aptitude for deciphering intricate patterns within large and heterogeneous datasets, holds immense promise in revolutionizing how we approach the identification, stratification, and management of heart disease [10]. These algorithms can assimilate multifaceted data sources, encompassing electronic health records, medical ima","cbCaikcM7fc4BauE","https://ap.wps.com/l/cbCaikcM7fc4BauE","pdf",327697,1,16,"English","en",105,"# Introduction\n## Cardiovascular diseases and the need for early detection\n## Role of machine learning in health prediction\n## Review objectives and scope\n# Importance of Early Heart Disease Prediction\n## Cleveland Heart Disease dataset and its role","[{\"question\":\"Why is early heart disease detection important according to the review?\",\"answer\":\"Early detection and intervention directly influence patient outcomes, healthcare costs, and broader societal well-being. The paper frames it as essential to improve clinical and public health impact.\"},{\"question\":\"What does the review cover about machine learning methods for heart disease detection?\",\"answer\":\"The review outlines machine learning fundamentals, explains prevalent algorithms used for heart disease detection, and surveys notable research applying ML techniques to diagnosis. It also provides a tabular comparison of studies to discuss strengths and weaknesses.\"},{\"question\":\"Which dataset is highlighted as a key resource in developing ML solutions?\",\"answer\":\"The Cleveland Heart Disease dataset from the UCI Machine Learning Repository is highlighted. It contains 303 instances and 14 attributes and has been used to develop and validate ML-driven cardiac risk assessment solutions.\"}]","Machine Learning Approaches for Heart Disease Detection - A Comprehensive Review | PDF",1785673074,40,{"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},"machine-learning-approaches-for-heart-disease-detection-a-comprehensive-review","",{"@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/machine-learning-approaches-for-heart-disease-detection-a-comprehensive-review/117011/",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 heart disease detection important according to the review?","Question",{"text":76,"@type":77},"Early detection and intervention directly influence patient outcomes, healthcare costs, and broader societal well-being. The paper frames it as essential to improve clinical and public health impact.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What does the review cover about machine learning methods for heart disease detection?",{"text":81,"@type":77},"The review outlines machine learning fundamentals, explains prevalent algorithms used for heart disease detection, and surveys notable research applying ML techniques to diagnosis. It also provides a tabular comparison of studies to discuss strengths and weaknesses.",{"name":83,"@type":74,"acceptedAnswer":84},"Which dataset is highlighted as a key resource in developing ML solutions?",{"text":85,"@type":77},"The Cleveland Heart Disease dataset from the UCI Machine Learning Repository is highlighted. It contains 303 instances and 14 attributes and has been used to develop and validate ML-driven cardiac risk assessment solutions.","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},[94,98,102,106,111,116,120,123,128,131,135],{"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":29,"slug":119},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},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":107,"slug":138},19,"General","general"]