[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121371-en":3,"doc-seo-121371-105":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},121371,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A Comparative Analysis of Machine Learning Models for Stroke Prediction - Research Article","Stroke is a leading global health burden, creating an urgent need to improve risk prediction and treatment decision-making. This research evaluates multiple machine learning approaches—Decision Trees, Random Forests, Neural Networks, Support Vector Machines (SVM), Elastic Net, and Lasso—using four cardiovascular and stroke datasets. Findings show Decision Trees and Random Forests consistently outperform Neural Networks in overall performance, while Neural Networks achieve promising accuracy. SVM delivers consistent results, whereas Elastic Net and Lasso provide average performance.","A Comparative Analysis of Machine Learning Models for Stroke  Prediction  \nKripa Mary Jose a, * , Nizar Banu a, A. Melvin Infant a  \nRESEARCH ARTICLE  \na Department of Computer Science and Engineering, Christ Deemed to be University Bangalore, Karnataka, India  \n* Corresponding Author: [j](josekripamary99@gmail.com)[osekripamary99@gmail.com](josekripamary99@gmail.com)  \nReceived: 29-01-2025, Revised: 06-04-2025, Accepted: 17-04-2025, Published: 22-04-2025  \nAbstract: Stroke is a leading global health burden, and there is an urgent need for improvement in risk prediction and treatment. This paper examines the capability of several machine learning algorithms, including Decision Trees, Random Forests, Neural Networks, Support Vector Machines (SVMs), Elastic Nets, and Lasso, to predict stroke risk on four cardiovascular and stroke datasets. The results indicate that Decision Trees and Random Forests are always better than Neural Networks, although Neural Networks show promising accuracy. SVMs are consistent, while the Elastic Net and Lasso models give average results.  \nKeywords: Stroke, Machine Learning, Risk Prediction, Decision Trees, Random Forests,  Neural Networks, Support Vector Machines, Elastic Net, Lasso, Neuroplasticity, Rehabilitation  \n1. Introduction  \nSevere medical condition-stroke-an instantaneous blockage within the system-the results of which, if nothing happens to reverse that blockage, can lead to serious devastations. According to definitions there are two distinct types: TIA-ischemic (and hemorrhagic) . TIA-ischemic takes place when one’s cerebral flow is diminished, primarily via a blood clot, lowering the oxygen reaching the cerebral cells. Hemorrhagic strokes, on the other hand, entail bleeding in the brain caused by the rupture of weaker blood vessels [1] . Both types of strokes require rapid medical intervention to prevent brain damage and enhance results. Mainly, symptoms include weak-ness or paralysis, numbness or tingling, trouble with speak-ing or speech understanding, impairment of vision, severe headaches, dizziness, and coordination lack [1] . Early detection is critical as it may substantially minimize the severity of the stroke, preventing further damage or even death. Many risk factors are involved in the development of stroke especially advancing ages such as high blood pressure, heart disease, diabetes, smoking, obesity, high cholesterol levels, physical inactivity, and excessive alcohol consumption. Prevention of stroke requires adequate management of these risk factors through lifestyle modification and pharmacological treatment.  \nML refers to artificial intelligence technology in support of systems learning patterns or rules from given data. Modern progress made in machine learning enables us to make stroke prediction models far more accurate than previously seen. Pre-vious studies have reported that the SVM algorithm achieved a high score of 96. 74% precision. This paper uses machine-learning algorithms for their performance in stroke prediction through cross-validation of various datasets. Here, Decision Trees, Random Forests, Neural Networks, Support Vector Machines, Elastic Nets, and Lasso are carefully tested to understand their predictiveness along with the appropriateness for stroke risk analysis [1, 2] .  \n2. Literature Review  \nIn their study, Nojood Alageel, RahafAlharbi, and col-leagues investigate the advantages of machine learning in stroke prediction using multiple datasets. They used Kaggle and local hospital datasets for stroke prediction and machine learning models like Stacking, Decision Tree, and Random Forest. Notably, they discovered that the NB classifier had the lowest accuracy level (86%), but other algorithms achieved comparable accuracies, f1 scores, precision, and recall [3] .  \nElias Dritsas, Maria Trigk, et al. drew emphasis on the substantial impact of stroke, which affects millions of people each year and causes death and disability. Their research finds f","cbCaikL9QIOQuJ5J","https://ap.wps.com/l/cbCaikL9QIOQuJ5J","pdf",1343207,1,17,"English","en",105,"# Introduction\n## Literature Review","[{\"question\":\"Which machine learning models are evaluated for stroke risk prediction?\",\"answer\":\"The study evaluates Decision Trees, Random Forests, Neural Networks, Support Vector Machines (SVM), Elastic Net, and Lasso on four cardiovascular and stroke datasets.\"},{\"question\":\"What performance trend is reported for Decision Trees and Random Forests compared with Neural Networks?\",\"answer\":\"Decision Trees and Random Forests are reported to be always better than Neural Networks, although Neural Networks still show promising accuracy.\"},{\"question\":\"How do SVM, Elastic Net, and Lasso perform in the comparison?\",\"answer\":\"SVM provides consistent results, while Elastic Net and Lasso yield average results in stroke risk prediction.\"}]","A Comparative Analysis of Machine Learning Models for Stroke Prediction - Research Article | PDF",1785735295,43,{"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},"a-comparative-analysis-of-machine-learning-models-for-stroke-prediction-research-article","",{"@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/a-comparative-analysis-of-machine-learning-models-for-stroke-prediction-research-article/121371/",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","2026-08-03",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},"Which machine learning models are evaluated for stroke risk prediction?","Question",{"text":75,"@type":76},"The study evaluates Decision Trees, Random Forests, Neural Networks, Support Vector Machines (SVM), Elastic Net, and Lasso on four cardiovascular and stroke datasets.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What performance trend is reported for Decision Trees and Random Forests compared with Neural Networks?",{"text":80,"@type":76},"Decision Trees and Random Forests are reported to be always better than Neural Networks, although Neural Networks still show promising accuracy.",{"name":82,"@type":73,"acceptedAnswer":83},"How do SVM, Elastic Net, and Lasso perform in the comparison?",{"text":84,"@type":76},"SVM provides consistent results, while Elastic Net and Lasso yield average results in stroke risk 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"]