[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119325-en":3,"doc-seo-119325-105":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":20,"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},119325,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Predicting Brain Stroke Using Machine Learning","The work reviews the global burden of stroke by sex and age groups using insights from the GBD 2013 study, then frames stroke as a time-critical medical emergency caused by disrupted cerebral blood supply. It highlights the importance of early symptom recognition and timely care to reduce severity and long-term outcomes. The study applies machine learning and deep learning for early prediction using a Kaggle stroke dataset. A Random Forest classifier achieves the best reported accuracy at 97%.","Predicting Brain Stroke Using Machine Learning  \nAproov Khare 1*, Sweta Kriplani2, Sneha Nema3, Rajnandni Soni4, Shantanu Mishra5, Rajendra Arakh6  \n1,2Professor, Department of Computer Science and Engineering, Shri Ram Institute of Technology, Jabalpur, India  \n3,4,5,6Department of Computer Science and Engineering, Shri Ram Institute of Technology, Jabalpur, India  \nAbstract—On the basis of the GBD (Global Burden of Disease) 2013 Study, this article provides an overview of the global, regional, and country-specific burden of stroke by sex and age groups, including trends in stroke burden from 1990 to 2013, and outlines recommended measures to reduce stroke burden [1]. The mind functions as the primary upper body organ for humans. A stroke is a medical condition wherein the blood vessels in the brain burst, resulting in brain damage. The interruption of blood and nutrient supply to the brain might cause symptoms. It is considered a medical emergency and might result in long-term neurological damage, complications, and sometimes death. According to the World Health Organization, stroke is the leading cause of death and disability globally. Early recognition of symptoms and seeking medical attention will reduce the disease’s severity. This paper uses deep learning and machine learning techniques to predict the possibility of a brain stroke occurring early-on. A reliable dataset for stroke prediction was acquired from Kaggle to test the effectiveness of the algorithm. The Random Forest classifier achieved the highest classification accuracy of 97% among the machine learning classifiers.  \nIndex Terms— Brain Stroke Prediction, Random Forest, Machine Learning, Stroke.  \n1. Introduction  \nThe functioning of the body's various parts is essential for human life. One significant threat to human life is a stroke, often detected more frequently in individuals over 65. Similar to how heart attacks affect the heart, strokes impact the brain. Strokes occur due to either restricted blood supply or ruptured blood vessels in the brain, leading to a lack of oxygen to brain tissues. Currently, strokes rank as the fifth leading cause of death globally. Timely medical care significantly improves a stroke victim's chances of recovery, as delayed treatment can result in death, disability, or brain damage. Stroke development can be influenced by various factors such as diet, inactivity, alcohol, tobacco, personal and medical history, and complications, as per the National Heart, Lung, and Blood Institute.  \nMagnetic resonance imaging (MRI) has emerged as a critical tool in clinical studies on brain anatomy [2] . MRI is the most frequently used medical imaging technique as it provides high resolution and contrast [3] . Stroke is a leading cause of mortality and morbidity worldwide, prompting significant efforts to develop effective predictive models for early detection and intervention. In this Python project, we aim to leverage machine learning techniques to predict the likelihood  \nof stroke occurrence in individuals based on various demographic, health, and lifestyle factors. By analyzing adataset containing features such as age, hypertension, heart disease history, glucose levels, BMI, gender, occupation, and smoking status, we endeavor to build a predictive model that can assist healthcare professionals in identifying individuals at higher risk of experiencing a stroke.  \nStrong data analysis tools are needed for big amounts of medical data. A substantial area of research in this field is on the use of artificial intelligence (AI) in medicine. The system can recognize which patients are most likely to develop the illness based on a patient's medical history. Through analysis of a patient's medical history, including age, blood pressure, sugar levels, and other factors, the technology can predict the risk that they will develop a disease. When there are a lot of factors, classification algorithms are employed to predict disease. A feed-forward mul","cbCailjI0KvTJYsr","https://ap.wps.com/l/cbCailjI0KvTJYsr","pdf",783563,1,7,"English","en",105,"# Introduction\n# Methodology\n## Data Preprocessing\n## Feature Selection","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper focuses on early prediction of brain stroke likelihood so that higher-risk individuals can be identified sooner for timely medical attention.\"},{\"question\":\"Which dataset and models are used for stroke prediction?\",\"answer\":\"A stroke prediction dataset is obtained from Kaggle, and the approach compares machine learning techniques, reporting Random Forest as the top performer.\"},{\"question\":\"How is data prepared before model training?\",\"answer\":\"The methodology includes one-hot encoding for categorical variables, label encoding for binary attributes like ever_married, and imputation for missing values such as filling BMI with the most frequent value.\"}]","Predicting Brain Stroke Using Machine Learning | PDF",1785723723,18,{"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},"predicting-brain-stroke-using-machine-learning","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-brain-stroke-using-machine-learning/119325/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address?","Question",{"text":75,"@type":76},"The paper focuses on early prediction of brain stroke likelihood so that higher-risk individuals can be identified sooner for timely medical attention.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and models are used for stroke prediction?",{"text":80,"@type":76},"A stroke prediction dataset is obtained from Kaggle, and the approach compares machine learning techniques, reporting Random Forest as the top performer.",{"name":82,"@type":73,"acceptedAnswer":83},"How is data prepared before model training?",{"text":84,"@type":76},"The methodology includes one-hot encoding for categorical variables, label encoding for binary attributes like ever_married, and imputation for missing values such as filling BMI with the most frequent value.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]