[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117535-en":3,"doc-seo-117535-105":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},117535,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Using Machine Learning Algorithms of Stroke Prediction","Stroke is a severe brain condition caused by disrupted blood flow or ruptured blood vessels, often resulting in death or long-term disability. Worldwide, stroke is recognized as a leading cause of mortality and morbidity, yet prediction research has received less attention than other cardiovascular threats. This work develops and evaluates multiple machine learning models using key physiological risk factors. Six algorithms are trained and tested to identify the approach with the highest predictive accuracy. Naïve Bayes achieves the strongest performance at about 82% accuracy, supporting early detection for clinical decision support. The study outlines preprocessing, model building, comparison, and future improvements through broader datasets and additional techniques.","Using Machine Learning Algorithms of Stroke Prediction  \nDaniela Slavkovska1, Anita Petreska2, Blagoj Ristevski2, Saso Nikolovski􀀕 􀀏 1LNROD 5HQGHYVNL 􀀕  \n1 Children’s Hospital Skopje  \n2 Faculty of Information and Communication Technologies-Bitola, University “St. Kliment Ohridski”-Bitola, Republic of North Macedonia  \n[dslavkovska@yahoo.com](dslavkovska@yahoo.com);[blagoj.ristevski@uklo.edu.mk](blagoj.ristevski@uklo.edu.mk);[etreska.anita@uklo.edu.mk](etreska.anita@uklo.edu.mk);  \n[sasnik@gmail.com](sasnik@gmail.com); [nikola.rendevski@uklo.edu.mk](nikola.rendevski@uklo.edu.mk)  \nAbstract:  \nStroke is a severe medical condition resulting from disrupted blood flow or ruptured blood vessels in the brain, often leading to life-threatening consequences. The World Health Organization (WHO) identifies stroke as a leading cause of death and disability worldwide. Although significant research has focused on heart-related diseases, stroke prediction has received comparatively less attention. To address this gap, this paper presents machine learning models developed to predict stroke likelihood, utilizing key physiological factors associated with stroke risk. Six algorithms: logistic regression, decision tree, random forest, KNN, SVMand Naïve Baye, were implemented to train and test prediction models. The primary objective was to determine the algorithm that provides the highest predictive accuracy.  \nOur findings reveal that the Naïve Bayes algorithm performed best, achieving an accuracy of approximately 82% . This is notable given Naïve Bayes’ suitability for probabilistic data and its efficiency in handling complex variable interactions, suggesting its value for early stroke detection in clinical settings. The use of machine learning in stroke prediction highlights a promising approach for early intervention, potentially aiding in reducing stroke-related mortality and morbidity.  \nThis paper contributes to expanding the application of machine learning in healthcare, emphasizing the need for focused stroke prediction research. Future work could enhance these models by integrating diverse datasets, testing additional machine learning techniques, and refining predictive algorithms to boost accuracy and reliability. By advancing stroke prediction, machine learning may play a key role in mitigating stroke’s impact on global health.  \nKeywords:  \nMachine Learning, Logistic Regression, Decision Tree Classification, Random Forest Classification, KNN, SVM and Naïve Bayes  \n1. Introduction  \nAbout 11% of all deaths worldwide are due to stroke Error! Reference source not found., according to the Centers for Disease Control and Prevention, strokes occur in the United States each year in about 795,000 people.  \nWith the advancement of medical technology, machine learning can now be used to predict stroke. It is possible to make accurate predictions and analyses using machine learning algorithms. Strokes can be predicted using machine learning algorithms.  \nSix different machine learning algorithms were tested, with Naive Bayes achieving the highest accuracy.  \nMachine learning algorithms are useful for making accurate predictions and delivering accurate analytics. Research conducted on stroke has mainly focused on predicting heart attacks. The main elements of the methods used and the results achieved show that of the five classification algorithms tested, Naïve Bayes showed the highest performance by achieving a superior accuracy metric. The limitation of this model lies in its training on textual data instead of actual real-time brain images. This paper shows how six machine learning algorithms were put into practice. This paper has the potential to be extended to include the implementation of all existing machine learning algorithms.  \nA database from Kaggle, which contains a range of physiological traits as its attributes, was used for this paper.  \nThese features are subsequently examined and used for final forecasting. The data is first cl","cbCaijCfPHbMqvEa","https://ap.wps.com/l/cbCaijCfPHbMqvEa","pdf",830433,1,11,"English","en",105,"# Abstract\n# Introduction\n# Machine learning algorithms for stroke prediction\n## Methodology\n## Data preprocessing\n# Methodology (continuation)","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses stroke prediction by using machine learning to estimate stroke likelihood based on physiological risk factors.\"},{\"question\":\"Which machine learning algorithm performs best in the study?\",\"answer\":\"The document reports that the Naïve Bayes algorithm achieves the highest performance, with accuracy of approximately 82%.\"},{\"question\":\"What dataset and preprocessing steps are used to build the models?\",\"answer\":\"A Kaggle dataset containing physiological traits is selected, cleaned by handling missing values, and converted to numeric form using label encoding and/or one-time encoding. The data is then split into training and testing sets before model construction and evaluation.\"}]","Using Machine Learning Algorithms of Stroke Prediction | PDF",1785676763,28,{"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},"using-machine-learning-algorithms-of-stroke-prediction","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/using-machine-learning-algorithms-of-stroke-prediction/117535/",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","2026-08-02",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},"What problem does the document address?","Question",{"text":75,"@type":76},"It addresses stroke prediction by using machine learning to estimate stroke likelihood based on physiological risk factors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithm performs best in the study?",{"text":80,"@type":76},"The document reports that the Naïve Bayes algorithm achieves the highest performance, with accuracy of approximately 82%.",{"name":82,"@type":73,"acceptedAnswer":83},"What dataset and preprocessing steps are used to build the models?",{"text":84,"@type":76},"A Kaggle dataset containing physiological traits is selected, cleaned by handling missing values, and converted to numeric form using label encoding and/or one-time encoding. 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