[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120628-en":3,"doc-seo-120628-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120628,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Investigating Machine Learning Algorithms for Stroke Occurrence Prediction","Stroke remains the leading cause of death and a major driver of long-term disability, making reliable risk prediction essential for timely intervention. This study trains six machine learning models—Random Forest, Decision Tree, K-Nearest Neighbour, Support Vector Classifier, Logistic Regression, and Stacking Classifier—using 10 stroke risk factors. Performance is assessed with AUC, precision, recall, F1-score, and accuracy. Experimental results show stacking classification achieves the highest performance, with AUC 98.80%, F1-score 95.18%, precision 95.08%, recall 95.41%, and accuracy 95.25%.","JCSI 37 (2025) 476–483 Received: 1 July 2025  \nAccepted: 3 November 2025  \nInvestigating Machine Learning Algorithms for Stroke Occurrence Prediction  \nKazeem B. Adedejia, *, Titilayo A. Ogunjobia, Thabane H. Shabangub, Joshua A. Omowayea  \na Department of Electrical and Electronics Engineering, Federal University of Technology, Akure, Ondo State, Nigeria b Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa  \nAbstract  \nStroke is the leading cause of death and the principal cause of long term disability. Accurate prediction of stroke is highly valuable for early intervention of treatment. In this study, six (6) machine learning (ML) algorithms namely: Random Forest (RF) classifier, Decision Tree (DT) classifier, K-Nearest Neighbour (KNN) classifier, Support Vector Classifier (SVC), Logistic Regression (LR) and Stacking Classifier (SC) were trained on 10 stroke risk factors to determine the most precise model for predicting the risk of stroke occurrence. The primary contribution of this work is the development of a stacking method that achieves high performance, as measured by various metrics such as Area under Curve (AUC), precision, recall, F1-score, and accuracy. The experimental results indicate that the stacking classification outperforms other methods, with an AUC of 98.80%, F1-score of 95.18%, precision of 95.08%, recall of 95.41%, and accuracy of 95.25%. The results revealed that the stacking classifier achieves a high performance and outperforms the other methods. With the rapid evolution of machine learning, the clinical professionals, and decision-makers can use the established models to assess the corresponding risk likelihood.  \nKeywords: Accuracy; data processing; machine learning; stroke prediction  \n*Corresponding author  \n[Email address](Email address: kbadedeji@futa.edu.ng)[:](Email address: kbadedeji@futa.edu.ng)[ kbadedeji@futa.edu.ng](Email address: kbadedeji@futa.edu.ng) (K. B. Adedeji)  \nPublished under Creative Common License (CC BY 4.0 Int.)  \n1. Introduction  \nStroke occurs due to the interruption of the flow of blood to a part of the brain as a result of blood clot. Globally stroke is one of the most severe diseases and it is directly responsible for a considerable number of death. According to the World Stroke Organization, 15 million people suffer a stroke each year out of these approximately 5 million people die as a result and another 5 million are left permanently disabled [1-5] . It is therefore considered as the leading causes of death and disability worldwide. It not only affects patients but also impacts their social environment, family, and workplace. Contrary to popular belief, stroke can happen to anyone, at any age, regardless of gender or physical condition. Each year, millions of stroke survivors have to adapt to a life with restrictions in daily activities. Many face problems such as memory, concentration, attention issues, speech difficulties, emotional problems, loss of balance, and difficulty swallowing [6]. Depending on the cause of stroke, stroke can be categorized into three; ischemic stroke, hemorrhagic stroke, and transient ischemic attack (TIA) as shown in Figure 1. In ischemic stroke, the arteries supplying blood to the brain completely become blocked. The hemorrhagic stroke occurs when an artery in the brain breaks leaks blood. As a result, the blood from that artery creates excess pressure in the skull and swells the brain, damaging brain cells and tissues. The TIA on the other hand is sometimes referred to as a mini stroke. It occurs when blood flow to the brain is blocked temporarily. While its symptoms are similar to those of hemorrhagic stroke, they  \ntypically disappear after a few minutes or hours when the blockage moves and blood flow is restored.  \nFigure 1: Classification of stroke [7] .  \nThe rising cost of hospitalization for stroke patients necessitates the development of advanced technologies to aid in clinical diagno","cbCaipjGtUjMQbM8","https://ap.wps.com/l/cbCaipjGtUjMQbM8","pdf",1191198,1,"English","en",105,"# Abstract\n# Introduction\n# Review of Related Studies","[{\"question\":\"Which machine learning algorithms are evaluated for stroke occurrence prediction?\",\"answer\":\"The study evaluates Random Forest, Decision Tree, K-Nearest Neighbour, Support Vector Classifier, Logistic Regression, and a Stacking Classifier.\"},{\"question\":\"What features are used to predict stroke occurrence?\",\"answer\":\"The models are trained using 10 stroke risk factors including age, gender, BMI, smoking status, heart disease, marital status, hypertension, and average glucose level.\"},{\"question\":\"How does the stacking classifier perform compared with other methods?\",\"answer\":\"The stacking classifier outperforms the other approaches, achieving AUC 98.80%, F1-score 95.18%, precision 95.08%, recall 95.41%, and accuracy 95.25%.\"}]","Investigating Machine Learning Algorithms for Stroke Occurrence Prediction | 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machine learning algorithms are evaluated for stroke occurrence prediction?","Question",{"text":74,"@type":75},"The study evaluates Random Forest, Decision Tree, K-Nearest Neighbour, Support Vector Classifier, Logistic Regression, and a Stacking Classifier.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What features are used to predict stroke occurrence?",{"text":79,"@type":75},"The models are trained using 10 stroke risk factors including age, gender, BMI, smoking status, heart disease, marital status, hypertension, and average glucose level.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the stacking classifier perform compared with other methods?",{"text":83,"@type":75},"The stacking classifier outperforms the other approaches, achieving AUC 98.80%, F1-score 95.18%, precision 95.08%, recall 95.41%, and accuracy 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