[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121335-en":3,"doc-seo-121335-105":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},121335,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Evaluation of COVID-19 mortality using machine learning regression methods based on health system indicators","The COVID-19 pandemic created major strain on healthcare systems and economies, making evaluation of healthcare-system performance during outbreaks a priority for policymakers and providers. This study investigates the relationship between healthcare system indicators and COVID-19 deaths by using machine learning regression models on complex, multi-factor datasets. Death counts per million population are analyzed for 27 OECD countries across 2006–2019, with healthcare indicators aggregated into accessibility, financing, and workforce dimensions. Random forest regression, neural network regression, and Gaussian process regression are compared via k-fold cross validation using R² and RMSE; results indicate Gaussian process regression achieves high predictive accuracy. Findings support prioritizing community-based services, technology adoption, public trust, social support, and leadership measures, alongside flexible PPE supply chain planning.","Sigma Journal of Engineering and Natural Sciences  \nWeb page info: [https://sigma.yildiz.edu.tr](https://sigma.yildiz.edu.tr)  \nDOI: 10.14744/sigma.2024.00032  \n| Research Article\u003Cbr>Evaluation of COVID-19 mortality using machine learning regression methods based on health system indicators\u003Cbr>Canan BULUT1, Tuğba SÖNAL2, Dilek KOLCA3, Fatih TARLAK4,*\u003Cbr>1İstanbul Kültür University, Department of Medical Services and Techniques, İstanbul, 34158, Türkiye 2Health Ministry of Turkey, Ataşehir District Health Directorate, İstanbul, 34100, Türkiye 3İstinye University, Department of Health Management, İstanbul, 34010, Türkiye 4Gebze Technical University, Deparment of Bioengineering, Kocaeli, 41400, Türkiye |\n| --- |\n|  |\n| ARTICLE INFO\u003Cbr>Article history\u003Cbr>Received: 11 September 2023\u003Cbr>Revised: 22 October 2023\u003Cbr>Accepted: 29 December 2023 Keywords:\u003Cbr>COVID-19; Health Indicator; Prediction; Machine Learning\u003Cbr>*Corresponding author.\u003Cbr>*[E-mail address: ftarlak@gtu.edu.tr](E-mail address: ftarlak@gtu.edu.tr)\u003Cbr>\u003Cbr>ABSTRACT\u003Cbr>In the global health crisis caused by the COVID-19 pandemic, countries have faced significant challenges in combating the outbreak in terms of healthcare systems and economies. Evaluating the performance of healthcare systems in dealing with pandemics has become a priority for policymakers, healthcare providers, and the public alike. Assessing the performance of healthcare systems during the pandemic is crucial for preparedness and improvements in similar situations in the future. By identifying complex patterns and relationships, machine learning algorithms aim to uncover the relationship between healthcare system indicators and deaths due to the COVID-19 pandemic, using large and intricate datasets. These algorithms utilize various datasets containing demographic information and medical factors to reveal hidden relationships between various variables and disease severity. The objective of this study is to predict COVID-19 death rates for 27 OECD (Organisation for Economic Co-operation and Development) countries spanning the period from 2006 to 2019 using various machine learning regression methods. Healthcare system indicators, comprising accessibility, healthcare financing, and healthcare workforce, have been aggregated into three dimensions. The dataset includes COVID-19 death counts pera million-population due to the pandemic. Random forest regression, neural network regression, and Gaussian process regression were employed to forecast COVID-19 death rates, and the predictive capabilities of machine learning regression methods were evaluated using k-fold cross validation. The suitability of the algorithms was assessed using statistical measures such asthe coefficient of determination (R²) and root mean square error (RMSE). A high R² value anda low RMSE indicate that Gaussian process regression (GPR) can effectively predict COVID-19 death rates, taking various health indicators into account. Machine learning regression methods have revolutionized our understanding of COVID-19 death rates. Through prediction models, machine learning has empowered healthcare professionals with the ability to forecast death risks for individual patients, guiding decision-making processes and resource allocation. According to the research findings, to enhance the performance of healthcare systems in coping with global pandemics, there is a need to prioritize community-based healthcare services, adopt a social policy approach, encourage the use of advanced technology, ensure the trust of the public and healthcare workers, enhance social support opportunities, emphasize the importance of measures by leaders, and support global governance. Additionally, flexible supply chain plans for the procurement of personal protective equipment have been identified as necessary.\u003Cbr>Cite this article as: Bulut C, Sönal T, Kolca D, Tarlak F. Evaluatıon of COVID-19 mortality using machine learning regression methods based on health system ind","cbCaip7QMM0PZ2bk","https://ap.wps.com/l/cbCaip7QMM0PZ2bk","pdf",971373,1,9,"English","en",105,"# Introduction\n## COVID-19 outbreak and healthcare challenges\n## Need for predictors and risk identification\n## Role of machine learning in outcome prediction","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To predict COVID-19 death rates for 27 OECD countries from 2006 to 2019 using machine learning regression methods based on health system indicators.\"},{\"question\":\"Which health system indicators are used in the analysis?\",\"answer\":\"Indicators are aggregated into three dimensions: accessibility, healthcare financing, and healthcare workforce.\"},{\"question\":\"How were the machine learning models evaluated?\",\"answer\":\"Models were assessed using k-fold cross validation and statistical metrics including R² and RMSE.\"}]","Evaluation of COVID-19 mortality using machine learning regression methods based on health system indicators | PDF",1785735124,23,{"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},"evaluation-of-covid-19-mortality-using-machine-learning-regression-methods-based-on-health-system-indicators","",{"@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/evaluation-of-covid-19-mortality-using-machine-learning-regression-methods-based-on-health-system-indicators/121335/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study?","Question",{"text":75,"@type":76},"To predict COVID-19 death rates for 27 OECD countries from 2006 to 2019 using machine learning regression methods based on health system indicators.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which health system indicators are used in the analysis?",{"text":80,"@type":76},"Indicators are aggregated into three dimensions: accessibility, healthcare financing, and healthcare workforce.",{"name":82,"@type":73,"acceptedAnswer":83},"How were the machine learning models evaluated?",{"text":84,"@type":76},"Models were assessed using k-fold cross validation and statistical metrics including R² and RMSE.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]