[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126160-en":3,"doc-seo-126160-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126160,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",7,"Healthcare","Comparative Analysis of KNN and Decision Tree Classification Algorithms for Early Stroke Prediction - A Machine Learning Approach","Stroke ranks among the most deadly diseases worldwide and causes substantial disability. Early symptom recognition and preventive action can reduce stroke mortality, motivating this research to apply information technology and machine learning to predict stroke risk for individuals. Using the CRISP-DM process, the study compares K-Nearest Neighbor and Decision Tree classifiers on a dataset of 5110 observations with 12 relevant attributes, including exploratory data analysis, preprocessing, and oversampling to address class imbalance. Results show the KNN model reaching about 97.1845% accuracy, supporting early identification of stroke risk and contributing to prevention efforts.","Comparative Analysis ofKNN and Decision Tree Classification Algorithms for Early Stroke Prediction: A Machine Learning Approach  \nKarin Eldora1, Erick Fernando2*, Winanti3  \n1,2Information Systems, Faculty of Engineering and informatics, Universitas Multimedia Nusantara, Indonesia  \n3Information Systems, Faculty of Technology and Information, Universitas Insan Pembangunan, Tangerang, Indonesia  \nEmail: [erick.fernando@umn.ac.id](erick.fernando@umn.ac.id)  \nAbstract  \nStroke is the second most deadly disease in the world and the third leading cause of disability. However, most deaths due to stroke can be prevented by recognizing the symptoms of stroke and taking preventive measures using information technology. Therefore, this research utilizes the role of information technology using a machine learning approach to predict stroke in a person using the K-Nearest Neighbor and Decision Tree classification methods. The two algorithms were compared to determine which algorithm was more effective in predicting stroke. Data analysis using the CRISPDM approach was carried out using a dataset containing 5110 observations with 12 relevant attributes. Implementation of Exploratory Data Analysis (EDA) was also carried out for preprocessing, and oversampling techniques were applied to overcome the problem of unbalanced classes. The research results show that the predictive model with the highest level of accuracy was obtained at around 97.1845% using the K-Nearest Neighbor algorithm. This research makes a significant contribution to stroke prevention efforts through the use of information technology and machine learning algorithms for early identification of stroke risk.  \nKeywords: Early Stroke, Prediction, Machine Learning, Comparison Algorithms, Classification, KNN, Decision Tree  \n1. INTRODUCTION  \nA stroke is a medical condition characterized by the interruption of blood flow toa part of the brain due to either a blockage or the rupture of a blood vessel in the brain [1], [2] . According to the World Health Organization (WHO), stroke is a manifestation of nerve function impairment resulting from cerebrovascular diseases, primarily stemming from non-traumatic cerebral circulation disorders[3],[4], [5] . This grim reality underscores why stroke is ranked as the second-leading cause of mortality and the third-leading cause of disability globally. In 2017, the  \nMinistry of Health of the Republic of Indonesia highlighted that stroke stands asthe foremost cause of both disability and death in Indonesia [6] . These statistics underscore the grave threat posed by stroke.  \nStroke risk factors commonly include conditions like chronic hypertension, diabetes, elevated blood sugar levels (hyperglycemia), high cholesterol levels (hyperlipidemia), obesity, and high blood pressure, among others [2], [7] . To reduce the likelihood of experiencing a stroke, individuals are advised to adopt a healthy lifestyle, incorporating regular physical activity and a balanced diet rich in fruits and vegetables.  \nMedical consensus emphasizes the critical role of early detection and immediate, appropriate treatment in stroke prevention, as these measures can significantly reduce the extent of brain damage and prevent potential complications [2], [5], [8] . Timely intervention is paramount, as untreated strokes can lead to prolonged brain damage, long-term disabilities, or even fatal outcomes [9] . The sooner an individual receives medical attention following a stroke, the lower the likelihood of severe damage, thereby minimizing the overall impact of stroke-related fatalities. This underscores the paramount importance of our research, which leverages machine learning algorithms for early stroke prediction, aiming to facilitate timely interventions that can ultimately reduce the incidence of strokerelated deaths and disabilities, thus improving overall public health outcomes [9],[10], [11], [12] .  \nHence, there is an urgent and significant need for substantial con","cbCainhFgYsmxS8v","https://ap.wps.com/l/cbCainhFgYsmxS8v","pdf",880356,8,1,26,"English","en",105,"# Introduction\n# Methods","[{\"question\":\"How does the research approach early stroke prediction?\",\"answer\":\"It uses machine learning classification, comparing K-Nearest Neighbor and Decision Tree to predict stroke risk based on patient data.\"},{\"question\":\"What methodology and dataset are used in the study?\",\"answer\":\"The study follows the CRISP-DM framework and analyzes a dataset with 5110 observations and 12 relevant attributes.\"},{\"question\":\"How was class imbalance handled during analysis?\",\"answer\":\"Exploratory data analysis was performed for preprocessing, and oversampling techniques were applied to overcome unbalanced classes.\"}]","Comparative Analysis of KNN and Decision Tree Classification Algorithms for Early Stroke Prediction - A Machine Learning Approach | PDF",1785903467,66,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"comparative-analysis-of-knn-and-decision-tree-classification-algorithms-for-early-stroke-prediction-a-machine-learning-approach","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/healthcare/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/comparative-analysis-of-knn-and-decision-tree-classification-algorithms-for-early-stroke-prediction-a-machine-learning-approach/126160/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"How does the research approach early stroke prediction?","Question",{"text":77,"@type":78},"It uses machine learning classification, comparing K-Nearest Neighbor and Decision Tree to predict stroke risk based on patient data.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What methodology and dataset are used in the study?",{"text":82,"@type":78},"The study follows the CRISP-DM framework and analyzes a dataset with 5110 observations and 12 relevant attributes.",{"name":84,"@type":75,"acceptedAnswer":85},"How was class imbalance handled during analysis?",{"text":86,"@type":78},"Exploratory data analysis was performed for preprocessing, and oversampling techniques were applied to overcome unbalanced classes.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,120,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":118,"slug":119},40,"healthcare",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]