[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123085-en":3,"doc-seo-123085-105":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":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},123085,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","COMPARATIVE MACHINE LEARNING ALGORITHM FOR CARDIOVASCULAR DISEASE PREDICTION - Vol. 6 - No. 4","Study presents a comparative machine learning approach for categorizing heart illness using the Cleveland UCI repository. The work covers dataset understanding, parameter selection, and predictive analytics development after data preparation. Using 303 samples with 14 features, multiple models are evaluated for accuracy based on percentage performance. Results show KNN (86%), Decision Trees (79%), Logistic Regression (85%), Naive Bayes (86%), and SVM (87%), with strong diagnostic performance. Random Forest achieves an 89% diagnostic rate via ROC analysis, supporting its effectiveness for cardiovascular prediction.","Vol. 06, No. 4 (2024) 1509-1522, doi: 10.24874/PES06.04.010  \nProceedings on Engineering Sciences  \n[www.pesjournal.net](www.pesjournal.net)  \nCOMPARATIVE MACHINE LEARNING ALGORITHM FOR CARDIOVASCULAR DISEASE  \nPREDICTION  \nAshish Mishra 1  \nJyoti Mishra Victor Hugo  \nAloísio Vieira Lira Neto  \nReceived 10.04.2024. Received in revised form 23.09.2024.  \nAccepted 16.10.2024. UDC – 004.032.26:616 .1  \nKeywords:  \nHeart Disease Prediction, Parameters, Machine Learning, Random Forest, Decision Tree  \nA B S T R A C T  \nIn the present study, It used to categorise heart illness in the Cleveland UCI repository. It visually describes the dataset, operational parameters, and predictive analytics development. Machine learning (ML) begins with data preparation. The technique uses an ML model and key parameters to predict cardiovascular illness in patients. The dataset comprises 14 heart disease characteristics for this investigation. The preliminary examination and evaluation predicted heart problems. The dataset has 303 samples with 14 features. The information is presented as a percentage of truth. KNN 86%, Decision Trees 79%, Logistic Regression 85%, Naive Bayes 86%, and Support Vector Machines 87% can predict heart disease 89% accurately. The receiver working characteristics show that the random forest technique for heart disease prediction has an 89% diagnostic rate. The proposed method uses the random forest algorithm since it has been shown to be the most effective algorithm for classifying cardiovascular illness.  \n© 2024 Published by Faculty of Engineering  \n1. INTRODUCTION  \nCardiovascular disease is a group of conditions that affect the heart and blood vessels in the body. There may also be damage to the arteries in the kidneys, heart, eyes, and brain. Cardiovascular diseases can be broken down into four main groups. Coronary Heart Disease is the first. This disease happens when blood flow to the heart muscle stops. This makes the heart work harder, which can cause angina, heart attacks, and heart failure. Here's the second group: strokes and transient ischemic attacks (Mishra, 2020) . These happen when blood flow to the brain is blocked or temporarily interrupted. When blood flow to the limbs is stopped, this third type of  \nheart disease happens. This causes a lot of pain in the legs, hair loss on the feet and legs, leg weakness, and sores that don't heal. The aorta, which is the body's largest blood vessel, is affected by the last type. This doesn't hurt, but when it bursts, it causes a lifethreatening blood loss.  \nHeart diseases and coronary heart diseases are types of cardiovascular illness. Coronary artery diseases (CAD) include angina and myocardial infarction (also called a heart attack) . In coronary heart diseases (CHD), plaque builds up inside the coronary vessels and causes heart problems. A heart attack is the top cause of death in the world, and if it is not treated quickly, it can lead to  \nserious health problems or even death. Cardiac dysfunction has become a difficult medical issue in recent years. Myocardial infarction kills one patient every minute. Systematizing the technique and educating the patient is essential due to the difficulties of predicting cardiac illness. Worldwide cardiovascular disease risk is high. A physician's cardiovascular risk assessment must be accurate and complete to reduce attack and stroke rates and improve cardiovascular protection (Mishra, 2019) . To avoid fatalities, it processes detecting these heart defects as soon as possible. Next, assess the user's cardiovascular disease risk. It solves cardiac disease detection issues, helping clinicians make better decisions. Medical specialists have collected a lot of data that can be analyzed. Hypothesis testing improves heart disease diagnosis and prognosis with machine learning. It is not unexpected that more compound algorithms (sets of rules), such as SVM and Random Forests, provided enhanced outcomes than those of simpler methods.","cbCaikaSRtY9xuqq","https://ap.wps.com/l/cbCaikaSRtY9xuqq","pdf",1575212,1,14,"English","en",105,"# Introduction\n## Cardiovascular disease overview\n## Coronary heart disease and clinical impact\n## Data-driven prediction with machine learning\n## Motivation for comparative algorithms","[{\"question\":\"What dataset and feature setup are used for cardiovascular disease prediction?\",\"answer\":\"The study uses the Cleveland UCI repository with 303 samples and 14 heart disease characteristics (14 features).\"},{\"question\":\"Which machine learning models are compared in the research?\",\"answer\":\"KNN, Decision Trees, Logistic Regression, Naive Bayes, and Support Vector Machines are compared, along with Random Forest for final diagnostic performance.\"},{\"question\":\"What accuracy or diagnostic performance is reported for Random Forest?\",\"answer\":\"ROC analysis indicates the Random Forest technique provides an 89% diagnostic rate for heart disease prediction.\"}]","COMPARATIVE MACHINE LEARNING ALGORITHM FOR CARDIOVASCULAR DISEASE PREDICTION - Vol. 6 - No. 4 | PDF",1785814566,35,{"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},"comparative-machine-learning-algorithm-for-cardiovascular-disease-prediction-vol-6-no-4","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/comparative-machine-learning-algorithm-for-cardiovascular-disease-prediction-vol-6-no-4/123085/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What dataset and feature setup are used for cardiovascular disease prediction?","Question",{"text":75,"@type":76},"The study uses the Cleveland UCI repository with 303 samples and 14 heart disease characteristics (14 features).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the research?",{"text":80,"@type":76},"KNN, Decision Trees, Logistic Regression, Naive Bayes, and Support Vector Machines are compared, along with Random Forest for final diagnostic performance.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy or diagnostic performance is reported for Random Forest?",{"text":84,"@type":76},"ROC analysis indicates the Random Forest technique provides an 89% diagnostic rate for heart disease prediction.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"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":106,"slug":138},19,"General","general"]