[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290753-105":59,"doc-detail-290753-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","machine-learning-for-cardiovascular-disease-risk-assessment-a-systematic-review","Machine Learning for Cardiovascular Disease Risk Assessment - A Systematic Review","","Accurate diagnosis and early detection of heart disease can save lives, yet predicting cardiovascular illness remains difficult when monitoring is impractical and data collection is time-consuming. This systematic review leverages database data mining and multiple machine learning models to support heart disease risk prognosis. The work evaluates algorithms including SVM, Naive Bayes, regression, KNN, AdaBoost, decision tree, XGBoost, and a voting ensemble approach. It emphasizes early detection, personalized treatment guidance, reduced healthcare costs, improved patient outcomes, and more efficient care delivery.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-for-cardiovascular-disease-risk-assessment-a-systematic-review/290753/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-for-cardiovascular-disease-risk-assessment-a-systematic-review/290753.png","ImageObject",300,407,{"name":92,"@type":93},"Riley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is early detection of heart disease important?","Question",{"text":113,"@type":114},"Early detection can improve survival because heart disease is a leading cause of mortality. Timely identification enables more effective treatment planning and intervention.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning algorithms are used for risk assessment?",{"text":118,"@type":114},"The review uses SVM, Naive Bayes, regression, KNN, AdaBoost, decision tree, XGBoost, and a voting ensemble method to model cardiovascular risk.",{"name":120,"@type":111,"acceptedAnswer":121},"What benefits does the document attribute to using machine learning for prediction?",{"text":122,"@type":114},"Machine learning is presented as enabling early detection, more personalized treatment, lower healthcare costs, better patient outcomes, and more efficient prioritization of high-risk patients.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290753,1789642504,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},1374391975076,"https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051","Machine Learning for Cardiovascular Disease Risk Assessment: A Systematic Review  \nDanish Quamar1, Mohammad Islam2  \n1Department Of Computer Science & Information Technology  \nMaulana Azad National Urdu University  \nHyderabad, India  \n[danishkhan.rock@gmail.com](danishkhan.rock@gmail.com)  \n2Assistant Professor  \nDepartment Of Computer Science & Information Technology  \nMaulana Azad National Urdu University  \nHyderabad, India  \n[islam@manuu.edu.in](islam@manuu.edu.in)  \nAbstract—Accurate diagnosis and early detection of heart disease can help save lives because it is the primary cause of mortality. Ifa forecast is inaccurate, patients could potentially suffer significant harm. Today, it is challenging to predict and identify heart disease. 24 hour monitoring is not practical due to the extensive equipment and time required. Heart disease treatments can be both expensive and challenging. In order to obtain the data from databases and use this information to successfully forecast cardiac illness, a variety of data mining techniques and machine learning algorithms are now accessible. We have used every technique to put the heart disease prognosis into practise. The algorithms used in SVM, NAIVE BAYER, REGRESSION, KNN, ADABOOST, DECISION TREE, and XG-BOOST And Voting Ensemble Method.  \nKeywords-Adaboost, XG-boost, Naive Bayes, SVM, Decision Tree, Random Forest, Logistic Regression, confusion matrix, and correlation matrix.  \nI. INTRODUCTION  \nHeart disease, also known as cardiovascular disease, is a term that refers to a range of conditions that affect the heart and blood vessels. These conditions can range from simple ones, such as arrhythmias or irregular heartbeats, to complex ones, such as heart failure or coronary artery disease. Heart disease is one of the leading causes of death worldwide and is a major public health concern. In this article, we will discuss the brief history and types of heart disease [1] .  \n1.1. Brief history:  \nThe history of heart disease can be traced back to ancient times. The ancient Egyptians believed that the heart was the center of the body's emotions and intelligence. They believed that the heart was the organ that weighed the soul after death. The ancient Greeks also believed that the heart was the center of the body's emotions and intelligence. They believed that the heart was the seat of the soul and the source of the body's energy [2] . In the 19th century, the French physician René Laennec invented the stethoscope, which allowed doctors to listen to the sounds of the heart and lungs. This innovation enabled doctors to diagnose heart disease for the first time. In the early 20th century, the German physician Werner Forssmann performed the first cardiac catheterization. This procedure allowed doctors to diagnose and treat heart disease by inserting a catheter into the heart [3] .  \n1.2. Types of heart disease:  \nThe most prevalent kind of cardiac illness is coronary artery disease (CAD) . When the arteries that carry blood to the heart are constricted or obstructed, it happens. This may result in angina, also known as chest pain or discomfort, or a heart attack. Heart failure: Heart failure happens when there is insufficient blood flow from the heart to the body. As a result, fluid may accumulate in the legs, lungs, and other areas of the body. Heart failure can be brought on by a number of conditions, such as CAD, hypertension, and diabetes.  \nArrhythmias: Abnormal heart beats are known as arrhythmias. They may be uneven, too quick, or too slow. A number of conditions, such as CAD, excessive blood pressure, and heart failure, can lead to arrhythmias.  \nValvular heart disease: When the heart's valves are damaged, valvular heart disease results. The resultant backflow of blood through the valve may result in cardiac failure. A number of things, including infections, congenital heart defects, and age, can lead to valvular heart disease.  \nCongenital heart disease: is a form of cardiovascular di","cbCaiqoV99s1xkJk","https://ap.wps.com/l/cbCaiqoV99s1xkJk","pdf",344741,11,"English","# Abstract\n# Introduction\n## Brief history\n## Types of heart disease\n## Health benefit","[{\"question\":\"Why is early detection of heart disease important?\",\"answer\":\"Early detection can improve survival because heart disease is a leading cause of mortality. Timely identification enables more effective treatment planning and intervention.\"},{\"question\":\"Which machine learning algorithms are used for risk assessment?\",\"answer\":\"The review uses SVM, Naive Bayes, regression, KNN, AdaBoost, decision tree, XGBoost, and a voting ensemble method to model cardiovascular risk.\"},{\"question\":\"What benefits does the document attribute to using machine learning for prediction?\",\"answer\":\"Machine learning is presented as enabling early detection, more personalized treatment, lower healthcare costs, better patient outcomes, and more efficient prioritization of high-risk patients.\"}]","Machine Learning for Cardiovascular Disease Risk Assessment - A Systematic Review | PDF",28]