[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126077-en":3,"doc-seo-126077-105":30,"detail-sidebar-cat-0-en-105":92},{"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":11,"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},126077,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Application of Machine Learning Techniques for Predicting Stroke Disease","Stroke is a cerebrovascular illness caused by a sudden interruption of blood flow to the brain, leading to neurological impairment. It remains a major global public health challenge, driving illness, mortality, and substantial socio-economic burden. Effective prevention and treatment require a strong understanding of current stroke realities. This research compares data mining approaches for stroke prediction using a Mayo Hospital Lahore dataset with 2326 instances and 11 attributes. Results indicate Naïve Bayes performs best, achieving 88.3% AUC and 80.8% accuracy with additional metric improvements.","Application of Machine Learning Techniques for Predicting Stroke Disease  \nMuhammad Yasir Rafiq1 , Awais Nazeer2 , Anusha Gilani 3*  \n1Assistant Professor, Higher Education Department Punjab; 2 Financial Analyst Forman Christian College, University ( A Chartered University); 3 Lecturer, Department of Information Systems, HSM, University of Management and Technology  \nKeywords: Stroke, Data Mining, Naïve Bayes, Classification, Machine Learning.  \nJournal Info:  \nSubmitted: November 05, 2024 Accepted:  \nNovember 16, 2024 Published:  \nNovember 19,2024  \nAbstract Stroke is a cerebrovascular illness caused by a sudden halt in blood flow to the brain, resulting in neurological impairment. Stroke is a major public health problem worldwide, affecting millions of people. It is a significant source of illness and mortality, imposing a significant socio-economic burden. A thorough awareness of the current global situation is required for effective treatments and preventive actions. This research compares data mining techniques for the prediction of stroke illness. Using a dataset obtained from Mayo Hospital, Lahore, that had 2326 instances, each with 11 attributes, we compared the performance of Support Vector Machine (SVM), Random Forest, Neural Network, and K-Nearest Neighbors (KNN) approaches. Orange Data Mining Software was applied to evaluate the data and execute machine learning techniques. The results show that Naïve Bayes is the best method for predicting the prevalence of Stroke disease. The proposed model demonstrates an Area Under the Curve (AUC) of 88.3%, an accuracy of 80.8%, and notable metrics including an F1-Score and precision.  \n*Correspondence author email address: [anushagilani93@gmail.com](anushagilani93@gmail.com)  \n[DOI:](DOI: 10.21015/vtcs.v12i2.1906)[ 10.21015/vtcs.v12i2.1906](DOI: 10.21015/vtcs.v12i2.1906)  \n1 Introduction  \nAccording to the World Stroke Organization [1], every year over 13 million people have a stroke, with approximately 5.5 million of them dying as a result of the condition. On a global scale, this disease is the leading cause of both mortality and disability, casting a significant shadow over many aspects of human life. It affects the affected people’s families, workplaces, and social networks in addition to themselves. It’s crucial to dispel the misconception that strokes are selective in their targets; they can strike anyone, irrespective of age, gender, or physical condition [2] .  \nA stroke is a sudden neurological disorder that affects the bloodvessels in the brain. It occurs when there is an interruption in the blood supply to a specific area of the brain, leading to a deficiency of oxygen for the brain cells. Strokes can be categorized into two main types: ischemic and hemorrhagic. They vary in severity and can cause  \nThis work is licensed under a Creative Commons Attribution 3.0 License.  \nVAWKUM Transactions on Computer Sciences Volume 12, Issue 2, 2024  \neither temporary or permanent damage. Hemorrhagic strokes are less common and result from the rupture of a blood vessel in the brain, leading to cerebral hemorrhage. In contrast, ischemic strokes, which are the most prevalent, happen when there is a restriction or blockage in an artery, causing a halt in blood flow to a particular area of the brain [3, 4] .  \nSeveral factors can significantly increase an individual’s risk of suffering a stroke. A prior history of stroke, or even the presence of a transient stroke (often referred to as a mini-stroke or TIA), is a clear risk factor. Additionally, the presence of myocardial infarction (heart attack) and other heart diseases, including heart failure and atrial fibrillation, can elevate the likelihood of a stroke occurrence[5] .  \nAge is another key determinant, with individuals aged 55 and above facing a higher risk, although it’s essential to note that strokes can occur at any age, even among children. Other risk factors include hypertension, carotid artery stenosis resulting from atherosclerosi","cbCaii0YWPTkc6fO","https://ap.wps.com/l/cbCaii0YWPTkc6fO","pdf",410500,1,14,"English","en",105,"# Introduction\n## Stroke overview and types\n## Risk factors\n## Symptoms and consequences\n# Methodology and evaluation\n## Dataset and attributes\n## Compared machine learning models\n## Performance assessment","[{\"question\":\"What causes a stroke and why is it a major public health issue?\",\"answer\":\"A stroke is caused by a sudden stop of blood flow to the brain, which impairs neurological function. It affects millions worldwide and contributes to both mortality and disability with significant socio-economic impact.\"},{\"question\":\"Which machine learning models are compared for stroke prediction in the research?\",\"answer\":\"The study compares Support Vector Machine (SVM), Random Forest, Neural Network, and K-Nearest Neighbors (KNN) using a hospital dataset, evaluated through Orange Data Mining Software.\"},{\"question\":\"Which technique performs best and what are its reported results?\",\"answer\":\"Naïve Bayes shows the best performance for predicting stroke prevalence, with an AUC of 88.3% and accuracy of 80.8%, along with strong precision and F1-Score metrics.\"}]","Application of Machine Learning Techniques for Predicting Stroke Disease | PDF",1785902934,35,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"application-of-machine-learning-techniques-for-predicting-stroke-disease","",{"@graph":36,"@context":86},[37,54,69],{"@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/application-of-machine-learning-techniques-for-predicting-stroke-disease/126077/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What causes a stroke and why is it a major public health issue?","Question",{"text":76,"@type":77},"A stroke is caused by a sudden stop of blood flow to the brain, which impairs neurological function. It affects millions worldwide and contributes to both mortality and disability with significant socio-economic impact.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are compared for stroke prediction in the research?",{"text":81,"@type":77},"The study compares Support Vector Machine (SVM), Random Forest, Neural Network, and K-Nearest Neighbors (KNN) using a hospital dataset, evaluated through Orange Data Mining Software.",{"name":83,"@type":74,"acceptedAnswer":84},"Which technique performs best and what are its reported results?",{"text":85,"@type":77},"Naïve Bayes shows the best performance for predicting stroke prevalence, with an AUC of 88.3% and accuracy of 80.8%, along with strong precision and F1-Score metrics.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]