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Early detection and accurate prediction of stroke occurrence are crucial for effective prevention and targeted interventions. This study proposes a machine learning-based approach to predict the likelihood of stroke among patients. A dataset with demographic, clinical, and lifestyle factors is used, with models including logistic regression, decision trees, random forests, and support vector machines to build a risk prediction model.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predictive-modelling-of-stroke-occurrence-among-patients-using-machine-learning-vol202355/290409/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predictive-modelling-of-stroke-occurrence-among-patients-using-machine-learning-vol202355/290409.png","ImageObject",300,407,{"name":92,"@type":93},"Eliana","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main objective of the study?","Question",{"text":112,"@type":113},"To develop and evaluate a machine learning predictive model that identifies patients with a high likelihood of stroke using historical data and multiple clinical and lifestyle variables.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which patient factors are included as variables in the dataset?",{"text":117,"@type":113},"The study considers demographic and clinical/lifestyle factors such as age, gender, hypertension, diabetes, smoking status, BMI, and medical history.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning approach performed best in predicting stroke risk?",{"text":121,"@type":113},"The supervised K-Nearest Neighbors (K-NN) model outperformed other methods, achieving an accuracy of 95%, with strong sensitivity and specificity.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290409,1789641629,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267","INTI JOURNAL | Vol.2023:55  \neISSN:2600-7320  \nPredictive Modelling of Stroke Occurrence among Patients using Machine  \nLearning  \nSures Narayasamy1*, Thilagamalar Maniam2  \n1Politeknik Nilai, Negeri Sembilan, Malaysia  \n2Hospital Cancelor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Malaysia  \n* Email: [suressamy@gmail.com](suressamy@gmail.com)  \nAbstract  \nStroke is a global public health concern with severe consequences. Early detection and accurate prediction of stroke occurrence are crucial for effective prevention and targeted interventions. This study proposes a machine learning-based approach to predict the likelihood of stroke among patients. A comprehensive dataset encompassing demographic, clinical, and lifestyle factors of a large patient cohort was employed. Variables such as age, gender, hypertension, diabetes, smoking status, BMI, and medical history were considered. Advanced machine learning algorithms, including logistic regression, decision trees, random forests, and support vector machines, were utilized to analyses the dataset and develop a predictive model. The results demonstrate that the machine learning-based approach achieved high predictive accuracy in identifying individuals at risk of stroke. The model exhibited excellent sensitivity and specificity, enabling effective stratification of patients based on their stroke likelihood. Developing an accurate stroke prediction model using machine learning holds immense potential for proactive healthcare strategies and personalized patient care. Early identification of high-risk patients enables timely intervention and implementation of preventive measures, potentially reducing the burden of stroke-related complications. This study showed that the supervised K-Nearest Neighbors Algorithm (K-NN) model outperforms the other methods, with an accuracy of 95% compared with other models.  \nKeywords  \nStroke prediction, Machine learning, Risk Predictive modelling, Healthcare interventions  \nIntroduction  \nStroke represents a significant global health challenge, accounting for a substantial proportion of morbidity and mortality worldwide. A stroke, also referred to as a brain attack, happens when a blood artery in the brain bursts or when blood flows to a certain area of the brain is restricted. Specific areas of the brain are damaged or lose function in both cases (National Heart, Lung, and Blood Institute, 2022) . According to the latest data published by the World Health Organization (WHO), stroke deaths in Malaysia alone reached 21,592 in 2020, accounting for approximately  \nSubmission: 7 October 2023; Acceptance: 2 November 2023  \nCopyright: © 2023. All the authors listed in this paper. The distribution, reproduction, and any other usage of the content of this paper is permitted, with credit given to all the author(s) and copyright owner(s) in accordance to common academic practice. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, as stated in the website: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nINTI JOURNAL | Vol.2023:55  \neISSN:2600-7320  \n12.85% of total deaths (World Health Organization, 2022) . These figures demonstrate the critical need for efficient stroke prevention measures and precise stroke risk prediction in patients.  \nThis emphasizes the significance of using more thorough and advanced techniques for stroke prediction. For instance, Dev et al.'s work from 2022 used machine learning and neural network to predict the occurrence of strokes among patients. With the help of a variety of factors age, heart disease, average glucose level, and hypertension, the researchers were able to identify those at risk of stroke with an astonishing 78% accuracy rate. This demonstrates how machine learning approaches can improve the estimation of the risk of stroke.  \nThe purpose of this study is to investigate how machine lea","cbCaicvEzS3Q6Hb4","https://ap.wps.com/l/cbCaicvEzS3Q6Hb4","pdf",187276,"English","# Abstract\n# Introduction\n# Methodology","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To develop and evaluate a machine learning predictive model that identifies patients with a high likelihood of stroke using historical data and multiple clinical and lifestyle variables.\"},{\"question\":\"Which patient factors are included as variables in the dataset?\",\"answer\":\"The study considers demographic and clinical/lifestyle factors such as age, gender, hypertension, diabetes, smoking status, BMI, and medical history.\"},{\"question\":\"Which machine learning approach performed best in predicting stroke risk?\",\"answer\":\"The supervised K-Nearest Neighbors (K-NN) model outperformed other methods, achieving an accuracy of 95%, with strong sensitivity and specificity.\"}]","Predictive Modelling of Stroke Occurrence among Patients using Machine Learning - Vol.2023:55 | PDF",15]