[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121271-en":3,"doc-seo-121271-105":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},121271,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Machine Learning Models for Prediction of COVID-19 Infection in North Macedonia","The COVID-19 pandemic, caused by the SARS-CoV-2 virus, created severe health and socio-economic impacts worldwide. Existing studies underline the importance of comorbidities for COVID-19 outcomes, yet accurate prediction remains challenging. This research evaluates machine learning algorithms to predict COVID-19 outcomes using an epidemiological dataset containing only positive cases from the Public Health Institute of North Macedonia. Models including KNN, Decision Tree, Logistic Regression, Random Forest, and ensemble approaches XGBoost and RUSBoost achieved high accuracy up to 90%, supporting data-driven forecasting when comorbidity information is available.","Machine Learning Models for Prediction of COVID-19 Infection in North Macedonia  \nMaja Kukusheva Paneva 1 , Cveta Martinovska Bande 2 , Natasha Stojkovikj 2 , Dushan Bikov 2  \n1 Faculty of Electrical Engineering, Goce Delcev University, Krste Misirkov, 10A, 2000 Stip, North Macedonia  \n2 Faculty of Computer Science, Goce Delcev University, Krste Misirkov, 10A, 2000 Stip, North Macedonia  \nAbstract –The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has emerged as one of the most significant global crises of this century, with severe health and socio-economic impacts worldwide. Existing research has highlighted the critical role ofcomorbidities in influencing COVID-19 outcomes, but effective prediction models remain a challenge. This study investigates the potential of machine learning algorithms to predict the outcomes of COVID-19 based on patients' comorbidities. The algorithms K-Nearest Neighbors, Decision Tree, Logistic Regression, and Random Forest are applied to an epidemiological dataset comprising only positive COVID-19 cases, obtained from the Public Health Institute of North Macedonia. Additionally, two ensemble learning techniques, XGBoost and RUSBoost, are used to enhance prediction accuracy. The models achieved high accuracy of 90% across the various algorithms. These findings suggest that machine learning models can be an effective tool for predicting COVID-19 outcomes, especially when comorbidity data is available.  \nKeywords – Machine learning, classification, ensemble methods, COVID-19 dataset  \nDOI: 10. 18421/TEM141-15  \n[https://doi.org/10.18421/TEM141-15](https://doi.org/10.18421/TEM141-15)  \nCorresponding author: Natasha Stojkovikj,  \nFaculty of Electrical Engineering, Goce Delcev University, Krste Misirkov, 10A, 2000 Stip, North Macedonia.  \nEmail: [natasa.stojkovik@ugd.edu. mk](natasa.stojkovik@ugd.edu. mk)  \n[Received: 09 July 2024](Received: 09 July 2024).  \nRevised: 23 October 2024.  \nAccepted: 24 December 2024.  \nPublished: 27 February 2025.  \n © 2025 Maja Kukusheva Paneva et al.; published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License.  \nThe article is published with Open Access at [https://www.temjournal.com/](https://www.temjournal.com/)  \n1. Introduction  \nThe initial outbreak of COVID-19 occurred in Wuhan, Hubei Province, China in late 2019. Recognized for its high contagion, COVID-19 attained pandemic status on March 12th, 2020, declared as such by the World Health Organization (WHO) . This declaration came amidst a surge in confirmed cases reported across numerous countries and regions globally. There were about 125 600 confirmed cases across 118 countries. A pandemic denotes the widespread prevalence of an infectious disease, reaching nearly every corner of the world [1],[2] .  \nThe symptoms ofCOVID-19 are fever, dry cough, fatigue, with occasional gastrointestinal symptoms. These symptoms are more severe in older adults with underlying chronic conditions, and many patients also experience shortness of breath, which can resemble flu-like symptoms. The virus is primarily transmitted through direct contact with respiratory droplets from an infected person, particularly through sneezing and coughing [3] .  \nExpert systems and other artificial intelligence techniques are crucial for diagnosing and containing the COVID-19 pandemic. Implementing these nontherapeutic approaches can alleviate significant pressure on healthcare systems by offering optimal diagnostic and predictive methods for managing 2019-nCoV effectively. The vaccination is used as a primary strategy to control the coronavirus (COVID- 19) pandemic, also known as SARS-CoV-2.  \nHowever, there remains insufficient data on how different clinical and sociodemographic factors impact outcomes related to COVID-19. Despite extensive global research for mortality and morbidity rates and the effects of various sociodemographic and clinical characteristics on COVID-19, gaps in unde","cbCaiujZaxxfPFco","https://ap.wps.com/l/cbCaiujZaxxfPFco","pdf",785675,1,9,"English","en",105,"# Introduction\n## COVID-19 background and transmission\n## Need for prediction models\n## Machine learning approaches in infectious disease forecasting","[{\"question\":\"Which machine learning models are used to predict COVID-19 outcomes?\",\"answer\":\"The study applies K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, and ensemble methods including XGBoost and RUSBoost.\"},{\"question\":\"What data is used for training and evaluation?\",\"answer\":\"The models use an epidemiological dataset that includes only confirmed positive COVID-19 cases obtained from the Public Health Institute of North Macedonia, focusing on patients’ comorbidities.\"},{\"question\":\"How accurate are the resulting prediction models?\",\"answer\":\"The models achieve high accuracy, reaching about 90% across the different algorithms used in the study.\"}]","Machine Learning Models for Prediction of COVID-19 Infection in North Macedonia | PDF",1785734819,23,{"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},"machine-learning-models-for-prediction-of-covid-19-infection-in-north-macedonia","",{"@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-prediction-of-covid-19-infection-in-north-macedonia/121271/",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},"Which machine learning models are used to predict COVID-19 outcomes?","Question",{"text":75,"@type":76},"The study applies K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, and ensemble methods including XGBoost and RUSBoost.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data is used for training and evaluation?",{"text":80,"@type":76},"The models use an epidemiological dataset that includes only confirmed positive COVID-19 cases obtained from the Public Health Institute of North Macedonia, focusing on patients’ comorbidities.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the resulting prediction models?",{"text":84,"@type":76},"The models achieve high accuracy, reaching about 90% across the different algorithms used in the study.","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"]