[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121114-en":3,"doc-seo-121114-105":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},121114,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Prediction of Stroke Disease Using Machine Learning Techniques","Stroke is a global condition with rising incidence and a major contribution to worldwide mortality. With progress in data analytics and machine learning, predictive modeling can reveal patterns that may support earlier insight and improved clinical decision-making. This study evaluates five widely used machine learning algorithms to build models for stroke prediction: Decision Tree, Logistic Regression, Linear Discriminant Analysis, Gaussian Naïve Bayes, and Support Vector Machine. Using a Jupyter Notebook Python-based workflow and 10-fold cross validation, the models classify outcomes into stroke and non-stroke, with Logistic Regression achieving the best accuracy at 50.93%.","PREDICTION OF STROKE DISEASE USING MACHINE LEARNING  \nTECHNIQUES  \n*Syarifah Adilah Mohamed Yusoff1, Saiful Nizam Warris2, Mohd Saifulnizam Abu Bakar3 and  \nRozita Kadar4  \n*[syarifah.adilah@uitm.edu.my](syarifah.adilah@uitm.edu.my1)[1](syarifah.adilah@uitm.edu.my1), [saifulwar@uitm.edu.my](saifulwar@uitm.edu.my2)[2](saifulwar@uitm.edu.my2), [mohdsaiful071@uitm.edu.my](mohdsaiful071@uitm.edu.my3)[3](mohdsaiful071@uitm.edu.my3),  \n[rozita231@uitm.edu.my](rozita231@uitm.edu.my4)[4](rozita231@uitm.edu.my4)  \n1,2,3,4Jabatan Sains Komputer & Matematik (JSKM),  \nUniversiti Teknologi MARA Cawangan `Pulau Pinang, Malaysia  \n*Corresponding author  \nABSTRACT  \nStroke is a global disease that is reported to increase annually and is a leading cause of mortality worldwide. The advancement of data analytics and machine learning has made it possible to foretell future patterns and insights, which could lead to the discovery of novel treatments for this condition. This study has investigated five commonly used machine learning algorithm to be constructed as potential models for predicting stroke dataset. Jupyter Notebook, aphyton-base engine, was employed as a data analytic tool for the purpose of analysing and evaluating all of the models. The five models were Decision Tree, Logistic Regression, Linear Discriminant Analysis, Gaussian Naïve Bayes and Support Vector Machine, have being implemented to predict binary outcome of stroke and no stroke. The accuracy percentage reported that Logistic regression outperformed other models with 50.93%.  \nKeywords: prediction, stroke, machine learning, data analytic, algorithm  \nIntroduction  \nThe number of people suffering from a stroke is increasing every day. Stroke or also known as “anginahmar” among local Malaysian community is a cerebrovascular disease that happen when blood flow to the brain is restricted, blocked or reduced. According to Department of Statistic Malaysia (2022), stroke is third leading death among Malaysian, after Ischaemic heart disease and Lower repository infections. Several studies recently indicate the increasing trends among low and middle income countries, compared to high-income countries and rising trend among young adults (Wen et al. (2021); Kay & Venketasubramaniah(2022)) .  \nArtificial intelligence is widely used in healthcare and medicine to assist doctors in making decisions by offering enhanced and accurate diagnosis and prognoses. Thanks to advancements in machine learning algorithms and statistical understanding, data mining and analytics have evolved beyond descriptive analysis to encompass predictive and prescriptive analytics. Figure 1 demonstrates the association of data mining with other disciplines, mutually union and complement each other to exploits three promising analytical approaches driven by information. The potential of these emerging  \ntechnologies goes beyond the medical field and permeates several aspects of real-life situations, including science and technology, as well as humanities and social sciences.  \nFigure 1: Analytics outcome from the emerging of data mining technology  \nIn this era of digitalization, information is regarded as valuable as oil. Data analytics have the potential to greatly impact current and future endeavours as a result of the extensive use of relevant technology. Data analytics has the ability to transform raw data into valuable insights, identifying trendsand solving problems. It can enhance corporate processes, optimise decision-making, and stimulate economic expansion.  \nResearch Methodology  \nThe general framework of supervised machine learning implementation is illustrated in the Figure 2, where the data of consist of stroke patients’ attributes were first went through some steps of preprocessing process in order to prepare prominent features for further analysis. Then, the data are split into testing and training randomly using 10-fold cross validation. The training data was used to construct the model based on sel","cbCaiaLR6NwLcmxB","https://ap.wps.com/l/cbCaiaLR6NwLcmxB","pdf",484738,1,11,"English","en",105,"# Abstract\n# Introduction\n# Research Methodology\n## Supervised Machine Learning Framework\n## Cross Validation\n# Understanding the Dataset\n## Dataset Size and Attributes\n## Feature Types and Target Definition\n## Exploratory Patterns and Records","[{\"question\":\"What is the goal of the study on stroke prediction?\",\"answer\":\"To build and evaluate machine learning models that predict stroke versus non-stroke outcomes using patient demographic and medical information.\"},{\"question\":\"Which machine learning algorithms were implemented?\",\"answer\":\"Decision Tree, Logistic Regression, Linear Discriminant Analysis, Gaussian Naïve Bayes, and Support Vector Machine.\"},{\"question\":\"How is model validation performed?\",\"answer\":\"The dataset is split into training and testing using 10-fold cross validation, with training used to construct models and testing used to validate predictions.\"}]","Prediction of Stroke Disease Using Machine Learning Techniques | PDF",1785733815,28,{"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},"prediction-of-stroke-disease-using-machine-learning-techniques","",{"@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/prediction-of-stroke-disease-using-machine-learning-techniques/121114/",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 is the goal of the study on stroke prediction?","Question",{"text":75,"@type":76},"To build and evaluate machine learning models that predict stroke versus non-stroke outcomes using patient demographic and medical information.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms were implemented?",{"text":80,"@type":76},"Decision Tree, Logistic Regression, Linear Discriminant Analysis, Gaussian Naïve Bayes, and Support Vector Machine.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model validation performed?",{"text":84,"@type":76},"The dataset is split into training and testing using 10-fold cross validation, with training used to construct models and testing used to validate predictions.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":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":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"]