[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119336-en":3,"doc-seo-119336-105":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},119336,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",7,"Healthcare","Stroke Prediction Based on Machine Learning - Comparative Evaluation of Random Forest and SVM","Stroke is a leading cause of death and disability worldwide, making early detection and prevention essential for reducing long-term damage. This study applies machine learning to analyze patients’ historical health data and biometrics for timely risk identification. After preprocessing missing values, categorical variables, and class imbalance, model performance is assessed using Random Forest and Support Vector Machine (SVM). Results show Random Forest reaches 95% accuracy and 93% precision, slightly outperforming SVM at 92% accuracy and 90% precision. Both methods still exhibit notable false-negative rates (12% vs. 15%), limiting clinical utility and motivating further optimization such as class-weight tuning or ensemble strategies.","Stroke Prediction Based on Machine Learning  \nYuhan Zhang  \nMarlan and Rosemary Bourns College of Engineering, University of California, Riverside, 92521, the United States  \nAbstract. Stroke has become an important cause of death and disability worldwide, which highlights the need for early detection and intervention.  \nMachine learning technology can analyze patients' historical health data and biometrics to identify high-risk individuals in a timely manner, thereby effectively predicting stroke.This paper evaluates the predictive performance Random Forest and Support Vector Machine (SVM) . Data preprocessing encompasses managing missing data, processing categorical variables, and tackling issues related to class imbalance. Analysis of the quantitative results indicates that the Random Forest model reaches an accuracy of 95% and a precision of 93%, providing a slight edge over the SVM, which records an accuracy of 92% and a precision of 90% ..  \nHowever, both models exhibit high false-negative rates, with Random Forest showing a false-negative rate of 12% and SVM at 15%, which significantly impacts their clinical utility. To improve performance, further model optimization, such as adjusting class weights or employing ensemble methods, is necessary to reduce these false-negative rates and enhance diagnostic accuracy. This study highlights the potential and limitations of machine learning in stroke prediction, showing that people need further optimization to enhance diagnostic performance.  \n1 Introduction  \nIn recent years, stroke has emerged as a major contributor to death and long-term disability globally, creating a substantial burden on individuals and healthcare systems alike. The prevalence of stroke has been rising globally, and in the United States, about 2.5 per cent of adults have experienced a stroke [1] . Every year, millions of people suffer from strokes, with factors such as high blood pressure, diabetes, smoking, and ageing contributing to the increasing prevalence of this life-threatening condition. Given the severe consequences, early detection and prevention of stroke are critical, as timely intervention can significantly reduce the risk of permanent damage or death. Over the past few years, machine learning (ML) has proven to be an effective tool in the healthcare sector. As the risk factors for stroke have become more apparent, ML has shown considerable promise in analyzing extensive patient data and detecting early indicators for stroke prediction.  \nMany researchers in the area of stroke risk prediction have conducted thorough investigations, employing machine learning models to enhance prediction accuracy. Alanzi et al. created machine learning models and implemented three data selection techniques: no  \n[Corresponding author: yzhan1111@ucr.edu](Corresponding author: yzhan1111@ucr.edu)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nresampling, imputation, and resampling [1] . Among the tested models, the Random Forest algorithm with data resampling outperformed others and achieved high accuracy (96%) in stroke prediction. Similarly, Shiozawa et al. explored the use of ML to predict stroke risk[2] . Their research demonstrated that machine learning could significantly improve prediction accuracy by analyzing larger datasets and incorporating more complex variables, such as lifestyle factors and genetic predispositions. They noted that handling data imbalances, especially with smaller stroke datasets, remains a critical challenge. The usefulness of ML models, such as Random Forest, XGBoost, Logistic Regression, and LightGBM, for forecasting the outcome of strokes was examined in a different study that used data from the Suita trial [3] . The predictive accuracy of Random Forest was found tobe superior to th","cbCaircDJLA6EEPI","https://ap.wps.com/l/cbCaircDJLA6EEPI","pdf",868916,1,10,"English","en",105,"# Introduction\n## Stroke risk factors and importance of early detection\n## Related machine learning studies\n# Methods\n## Data and preprocessing","[{\"question\":\"What problem does the study address in stroke care?\",\"answer\":\"The study targets early stroke risk detection by predicting high-risk individuals using machine learning from historical health data and biometrics.\"},{\"question\":\"How does the paper prepare the dataset before training models?\",\"answer\":\"Preprocessing includes handling missing data, processing categorical variables, and addressing class imbalance to improve training reliability.\"},{\"question\":\"Which model performs better, and what is the main limitation?\",\"answer\":\"Random Forest achieves 95% accuracy and 93% precision, slightly better than SVM (92% accuracy, 90% precision). Both models show high false-negative rates (12% for Random Forest, 15% for SVM), reducing clinical utility.\"}]","Stroke Prediction Based on Machine Learning - Comparative Evaluation of Random Forest and SVM | PDF",1785723770,25,{"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-based-on-machine-learning-comparative-evaluation-of-random-forest-and-svm","",{"@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-based-on-machine-learning-comparative-evaluation-of-random-forest-and-svm/119336/",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},"What problem does the study address in stroke care?","Question",{"text":75,"@type":76},"The study targets early stroke risk detection by predicting high-risk individuals using machine learning from historical health data and biometrics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper prepare the dataset before training models?",{"text":80,"@type":76},"Preprocessing includes handling missing data, processing categorical variables, and addressing class imbalance to improve training reliability.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs better, and what is the main limitation?",{"text":84,"@type":76},"Random Forest achieves 95% accuracy and 93% precision, slightly better than SVM (92% accuracy, 90% precision). Both models show high false-negative rates (12% for Random Forest, 15% for SVM), reducing clinical utility.","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},[93,97,101,105,110,115,118,123,128,131,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]