[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120131-en":3,"doc-seo-120131-105":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},120131,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Models for the Identification of Cardiovascular Diseases Using UK Biobank Data - Abstract and Study Overview","Machine learning models are developed to identify cardiovascular diseases (CVD) early and accurately within primary healthcare settings, enabling timely intervention for better treatment and management. The study addresses limitations of traditional population-based CVD risk models that often overlook differences in lifestyle, socioeconomic context, and genetic predisposition. Using UK Biobank primary healthcare data, the work trains and compares multiple explainable models and selects the best-performing approaches for CVD detection.","Machine Learning Models for the Identification of Cardiovascular Diseases Using UK Biobank Data  \nSheikh Mohammed Shariful Islam 1 , Moloud Abrar2 , Teketo Tegegne 1 , Liliana Loranjo3 , Chandan Karmakar2 , Md Abdul Awal4 , Md. Shahadat Hossain5 , Muhammad Ashad Kabir6 , Mufti Mahmud7 , Abbas Khosravi2 , George Siopis8 , Jeban C Moses 1 , Ralph Maddison 1  \n1 Institute for Physical Activity and Nutrition, Deakin University, Melbourne, Australia  \n2 School of Information Technology, Deakin University, Melbourne, Australia  \n3 Westmead Applied Research Centre, The University of Sydney, Sydney, Australia  \n4 School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia  \n5 School of Computer, Data, and Mathematical Sciences, Western Sydney University, Sydney, Australia  \n6 School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, Australia  \n7 Department of Computer Science, Nottingham Trent University, Nottingham, UK  \n8 Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia  \nCorresponding author: [shariful.islam@deakin.edu.au](shariful.islam@deakin.edu.au) and [akabir@csu.edu.au](akabir@csu.edu.au)  \nAuthor emails: [shariful.islam@deakin.edu.au](shariful.islam@deakin.edu.au) , [m.abdar@deakin.edu.au](m.abdar@deakin.edu.au) , [teketo.tegegne@deakin.edu.au](teketo.tegegne@deakin.edu.au) , [liliana.laranjo@sydney.edu.au](liliana.laranjo@sydney.edu.au) , [karmakar@deakin.edu.au](karmakar@deakin.edu.au) , [m.awal@uq.edu.au](m.awal@uq.edu.au) , [shahadat_qs@iubat.edu](shahadat_qs@iubat.edu) , [akabir@csu.edu.au](akabir@csu.edu.au) , [mufti.mahmud@ntu.ac.uk](mufti.mahmud@ntu.ac.uk) , [abbas.khosravi@deakin.edu.au](abbas.khosravi@deakin.edu.au) , [georgios.siopis@students.mq.edu.au](georgios.siopis@students.mq.edu.au) , [jcmoses@deakin.edu.au](jcmoses@deakin.edu.au) , [ralph.maddison@deakin.edu.au](ralph.maddison@deakin.edu.au)  \nKeywords: Machine Learning, Heart Diseases, Primary Prevention, Artificial Intelligence, Prediction.  \nABSTRACT  \nBackground: Machine learning models have the potential to identify cardiovascular diseases (CVDs) early and accurately in primary healthcare settings, which is crucial for delivering timely treatment and management. Although population-based CVD risk models have been used traditionally, these models often do not consider variations in lifestyles, socioeconomic conditions, or genetic predispositions. Therefore, we aimed to develop machine learning models for CVD detection using primary healthcare data, compare the performance of different models, and identify the best models.  \nMethods: We used data from the UK Biobank study, which included over 500,000 middleaged participants from different primary healthcare centers in the UK. Data collected at baseline (2006--2010) and during imaging visits after 2014 were used in this study. Baseline characteristics , including sex, age, and the Townsend Deprivation Index , were included. Participants were classified as having CVD if they reported at least one of the following conditions: heart attack, angina, stroke, or high blood pressure. Cardiac imaging data such as electrocardiogram and echocardiography data , including left ventricular size and function, cardiac output, and stroke volume , were also used. We used 9 machine learning models (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA), which are explainable and easily interpretable. We reported the accuracy, precision, recall, and F- 1 scores; confusion matrices; and area under the curve (AUC) curves.  \nResults: RBFSVM, GP, DT, and AdaBoost were the best performing models (accuracy: 96) , and LSVM showed the weakest prediction performance (accuracy: 0.69) . While RBFSVM and GP could significantly classify people with CVD better, LSVM and AdaBoost could classify healthy participants more accurately. The LSVM has a better false positive rate but a worse false negative rate. Moreover, the RBFSVM and","cbCaipEOAjhj2QbX","https://ap.wps.com/l/cbCaipEOAjhj2QbX","pdf",625592,1,19,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Introduction\n## Global burden and prevention of CVD\n## Diagnostic challenges and need for early detection","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To develop machine learning models for CVD detection using primary healthcare data, compare different model performances, and identify the best-performing methods.\"},{\"question\":\"What data sources and participant criteria are used?\",\"answer\":\"The study uses UK Biobank data from over 500,000 middle-aged participants, using baseline data (2006–2010) and imaging visits after 2014, and labels participants as having CVD based on reported heart attack, angina, stroke, or high blood pressure.\"},{\"question\":\"Which models performed best for identifying CVD?\",\"answer\":\"RBFSVM, GP, DT, and AdaBoost were reported as the best-performing models, while LSVM showed weaker prediction performance. RBFSVM, GP, DT, NN, and AdaBoost achieved the best AUC values for CVD detection.\"}]","Machine Learning Models for the Identification of Cardiovascular Diseases Using UK Biobank Data - Abstract and Study Overview | PDF",1785728362,48,{"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},"machine-learning-models-for-the-identification-of-cardiovascular-diseases-using-uk-biobank-data-abstract-and-study-overview","",{"@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/machine-learning-models-for-the-identification-of-cardiovascular-diseases-using-uk-biobank-data-abstract-and-study-overview/120131/",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 is the main goal of this study?","Question",{"text":75,"@type":76},"To develop machine learning models for CVD detection using primary healthcare data, compare different model performances, and identify the best-performing methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources and participant criteria are used?",{"text":80,"@type":76},"The study uses UK Biobank data from over 500,000 middle-aged participants, using baseline data (2006–2010) and imaging visits after 2014, and labels participants as having CVD based on reported heart attack, angina, stroke, or high blood pressure.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models performed best for identifying CVD?",{"text":84,"@type":76},"RBFSVM, GP, DT, and AdaBoost were reported as the best-performing models, while LSVM showed weaker prediction performance. RBFSVM, GP, DT, NN, and AdaBoost achieved the best AUC values for CVD detection.","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":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]