[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118437-en":3,"doc-seo-118437-105":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},118437,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Stroke Prediction Using Machine Learning - Early Detection and Risk Assessment Study","Stroke is a cerebrovascular event causing major global morbidity and mortality, triggered by sudden interruption or reduction of brain blood supply. Early detection is essential for effective intervention, so this study builds machine learning–based tools for stroke risk assessment. Logistic regression, random forest, naive Bayes, and support vector machine models are used with workflows covering data cleaning, class imbalance handling, model evaluation, and deployment.","Stroke Prediction Using Machine Learning  \nNiharika Patil and Alex Sumarsono  \n1. Department of Engineering, California State University, East Bay, Hayward, United States  \nAbstract:  \nStroke, a cerebrovascular event, represents a significant global health concern due to its substantial impact on morbidity and mortality. It occurs when there is a sudden interruption or reduction of blood supply to the brain, leading to the impairment of brain function. As the second leading cause of death globally, stroke demands urgent attention, and early detection is pivotal for effective intervention. This study addresses the global health concern of strokes by leveraging machine learning models for early detection and risk assessment. The study employs logistic regression, random forest, naive Bayes, and support vector machine algorithms to create a robust predictive model. Key objectives include data cleaning, addressing class imbalance, model evaluation, and deployment. The research contributes to the growing literature on machine learning applications in healthcare by presenting a holistic approach to stroke prediction. Results indicate that while random forest achieves high accuracy, logistic regression provides a balanced sensitivity-specificity trade-off. The models are deployed through an interactive Shiny app, enhancing accessibility and usability for healthcare professionals. Future work involves refining models, incorporating additional features.  \nKeywords: Stroke, machine learning models, predictive model, risk assessment, Shiny app deployment.  \nINTRODUCTION  \nIn recent years, the intersection of healthcare and machine learning has presented unprecedented opportunities for enhancing predictive analytics and improving patient outcomes [1] . Among the myriad of medical conditions, stroke stands out as a leading cause of morbidity and mortality worldwide [2] . Early detection and timely intervention are critical factors in mitigating the devastating effects of strokes. This paper explores a machine learning approach to stroke prediction.  \nStroke, characterized by a sudden interruption of blood flow to the brain, poses a significant public health challenge [3] . Machine learning models have shown promise in analyzing complex patterns within large datasets, facilitating the identification of subtle risk factors, and improving the accuracy of predictive models [4] .  \nKey objectives ofthis study include:  \n• To perform data cleaning and preparation including splitting data into training and testing.  \n• To deal with class imbalance.  \n• To evaluate and select the best suited predictive model.  \n• To deploy the model for use.  \nThis paper contributes to the growing literature on machine learning applications in healthcare  \nby presenting a holistic approach to stroke prediction. The remaining sections of this paper are structured as follows: Section II delves into the existing literature on the subject. Section III outlines the methodology employed in this study. Section IV provides a detailed analysis of the results; Section V shows web application deployment of the model and Section VI concludes the paper.  \nRELATED WORK  \nThe logistic regression model, recognized as the logit model, is extensively utilized in classification and predictive analytics. The estimation of the probability of a specific event occurrence, such asthe likelihood of an individual having a stroke, is conducted based on a dataset of independent variables. The outcome is a probability confined within the 0 to 1 range, with predictions for binary classification made by interpreting probabilities-0.5 or less predicts 0, while over 0 predicts 1. The random forest algorithm, a frequently employed machine learning approach, amalgamates outputs from multiple decision trees to derive a single result. An ensemble of decision trees, each constructed from a bootstrap sample drawn with replacement from a training set, constitutes the random forest. Feature bagging intro","cbCaigJRSNhfcYNE","https://ap.wps.com/l/cbCaigJRSNhfcYNE","pdf",592542,1,12,"English","en",105,"# INTRODUCTION\n# RELATED WORK\n## Machine Learning Models for Classification\n# METHODOLOGY\n# RESULTS AND ANALYSIS\n# WEB APPLICATION DEPLOYMENT\n# CONCLUSION","[{\"question\":\"What problem does the study address and why is early detection important?\",\"answer\":\"The study targets stroke prediction for earlier detection, since timely intervention can mitigate severe outcomes. Stroke is highlighted as a leading cause of morbidity and mortality worldwide.\"},{\"question\":\"Which machine learning algorithms are used in the predictive model?\",\"answer\":\"The study employs logistic regression, random forest, naive Bayes, and support vector machine (SVM). These models are built for robust stroke prediction and risk assessment.\"},{\"question\":\"How are the models evaluated and deployed for real use?\",\"answer\":\"The process includes data cleaning, handling class imbalance, and evaluating models to select suitable performance. The final models are deployed through an interactive Shiny app to support healthcare professionals.\"}]","Stroke Prediction Using Machine Learning - Early Detection and Risk Assessment Study | PDF",1785683606,30,{"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},"stroke-prediction-using-machine-learning-early-detection-and-risk-assessment-study","",{"@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/stroke-prediction-using-machine-learning-early-detection-and-risk-assessment-study/118437/",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 study address and why is early detection important?","Question",{"text":75,"@type":76},"The study targets stroke prediction for earlier detection, since timely intervention can mitigate severe outcomes. Stroke is highlighted as a leading cause of morbidity and mortality worldwide.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are used in the predictive model?",{"text":80,"@type":76},"The study employs logistic regression, random forest, naive Bayes, and support vector machine (SVM). These models are built for robust stroke prediction and risk assessment.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated and deployed for real use?",{"text":84,"@type":76},"The process includes data cleaning, handling class imbalance, and evaluating models to select suitable performance. 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