[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117479-en":3,"doc-seo-117479-105":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},117479,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Using Machine Learning to Predict Stroke - Master's Thesis","Stroke is a major cause of death and long-term disability in the United States and worldwide, and earlier prediction is presented as a way to reduce mortality. The thesis proposes a predictive framework using five machine learning models—Logistic Regression, Random Forest, Gradient Boosting, Decision Tree, and Support Vector Machine—on a Kaggle dataset of 5,110 records and 12 columns (1 target, 11 features). Because the dataset is imbalanced, SMOTETomek and SVM-SMOTE oversampling are applied and results are compared before and after resampling. Logistic Regression and SVC show strong performance before oversampling, while Gradient Boosting performs best after oversampling, with SVMSMOTE improving overall model accuracy.","CALIFORNIA STATE UNIVERSITY SAN MARCOS  \nTHESIS SIGNATURE PAGE  \nTHESIS SUBMITTED IN PARTIAL FULFILLMENT  \nOF THE REQUIREMENTS FOR THE DEGREE  \nMASTER OF SCIENCE  \nIN  \nCOMPUTER SCIENCE  \nTHESIS TITLE: Using Machine Learning to Predict Stroke  \nAUTHOR: Yajuan She  \nDATE OF SUCCESSFUL DEFENSE: 12/5/2023  \nTHE THESIS HAS BEEN ACCEPTED BY THE THESIS COMMITTEE IN  \nPARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE.  \nAhmad Hadaegh  \n\n| THESIS COMMITTEE CHAIR\u003Cbr>Lutfor Rahman | SIGNATURE | DATE |\n| --- | --- | --- |\n\nTHESIS COMMITTEE MEMBER  \nSIGNATURE  \nDATE  \nYajuan She  \n[she002@cougars.csusm.edu](she002@cougars.csusm.edu)  \nDepartment of Computer Science and Information  \nSystem  \nUsing Machine Learning to Predict Stroke  \nTable of Contents  \nAbstract ............................................................................................................................. 4  \nChapter 1 Introduction ........................................................................................................ 5  \nChapter 2 Related Work ..................................................................................................... 6  \nChapter 3 Methodology....................................................................................................... 8  \n3.1 Dataset ..................................................................................................................... 8  \n3.2 Exploratory Data Analysis .......................................................................................... 9  \n3.3 Methodology ........................................................................................................... 11  \n3.4 Data Preprocessing ................................................................................................. 11  \n3.5 SMOTE ................................................................................................................... 12  \n3.6 Machine Learning Algorithm..................................................................................... 12  \n3.7 Evaluation Measure ................................................................................................. 14  \nChapter 4 Experiments and Result .................................................................................... 16  \n4.1 Results without Oversampling .................................................................................. 16  \n4.2. Results with SMOTETomek .................................................................................... 17  \n4.3. Results with SVMSMOTE ....................................................................................... 19  \nChapter 5 Conclusion and Future Work ............................................................................. 21  \nReference ........................................................................................................................ 22  \nAbstract  \nStroke is a leading cause of death in the United States and is a major cause of serious disability for adults. According to the World Health Organization (WHO), stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. About 800,000 people in the US die yearly; about three in four are first-time strokes. Strokes are also the leading cause of long-term disability and the leading preventable cause of disability. The prediction of stroke in advance will help reduce the death rate. In recent years, predicting stroke in the real-life medical area has not been easy. A massive amount of healthcare data was collected for analysis. This paper proposes predicting stroke using five machine learning algorithms: Logistics Regression, Random Forest Classifier, Gradient Boosting Classifier, Decision Tree Classifier, and Support Vector Machine. The dataset we are using is from Kaggle. There are 5110 records and 12 columns. It includes one target and 11 features such as gender, age, hypert","cbCaigqaHsxX45Wq","https://ap.wps.com/l/cbCaigqaHsxX45Wq","pdf",460513,1,23,"English","en",105,"# Abstract\n# Chapter 1 Introduction\n# Chapter 2 Related Work\n# Chapter 3 Methodology\n## Dataset\n## Exploratory Data Analysis\n## Data Preprocessing\n## SMOTE\n## Machine Learning Algorithm\n## Evaluation Measure\n# Chapter 4 Experiments and Result\n## Results without Oversampling\n## Results with SMOTETomek\n## Results with SVM-SMOTE\n# Chapter 5 Conclusion and Future Work\n# References","[{\"question\":\"Why is stroke prediction emphasized in the study?\",\"answer\":\"Stroke is a medical emergency that can cause death quickly and lead to long-term disability. Predicting stroke early is intended to support timely treatment and reduce death rates.\"},{\"question\":\"What dataset and features are used for the prediction task?\",\"answer\":\"The study uses a Kaggle dataset containing 5,110 records and 12 columns, with 1 target and 11 features such as gender and age, including clinical risk factors.\"},{\"question\":\"How does the thesis address class imbalance during modeling?\",\"answer\":\"Because the dataset is imbalanced, the thesis applies oversampling methods including SMOTETomek and SVM-SMOTE, then compares model performance before and after resampling.\"}]","Using Machine Learning to Predict Stroke - Master's Thesis | PDF",1785676094,58,{"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},"using-machine-learning-to-predict-stroke-masters-thesis","",{"@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/using-machine-learning-to-predict-stroke-masters-thesis/117479/",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","2026-08-02",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},"Why is stroke prediction emphasized in the study?","Question",{"text":75,"@type":76},"Stroke is a medical emergency that can cause death quickly and lead to long-term disability. Predicting stroke early is intended to support timely treatment and reduce death rates.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and features are used for the prediction task?",{"text":80,"@type":76},"The study uses a Kaggle dataset containing 5,110 records and 12 columns, with 1 target and 11 features such as gender and age, including clinical risk factors.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis address class imbalance during modeling?",{"text":84,"@type":76},"Because the dataset is imbalanced, the thesis applies oversampling methods including SMOTETomek and SVM-SMOTE, then compares model performance before and after resampling.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},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"]