[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126328-en":3,"doc-seo-126328-105":31,"detail-sidebar-cat-0-en-105":89},{"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},126328,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Performance Evaluation on COVID-19 Prediction using Machine Learning Models - read online free","The COVID-19 pandemic has significantly strained international healthcare delivery while strengthening the need for more reliable forecasting methods. Many existing approaches struggle with long-term consequences and with capturing the broad impact across diverse locations and populations. This project evaluates machine learning models to improve COVID-19 trend prediction accuracy in long-horizon forecasting. It uses systematic review insights, extensive data collection, and rigorous model formulation and testing.","Journal of Informatics and  \nWeb Engineering  \nVol. 4 No. 2 (June 2025)  \neISSN: 2821-370X  \nPerformance Evaluation on COVID-19 Prediction using Machine Learning Models  \nObai Ali Abderlahman1, Naveen Palanichamy2*, Su-Cheng Haw3, Subhashini Gopal4  \n1,2,3Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Malaysia  \n4St. Joseph’s Institute of Technology, OMR, Chennai, Tamil Nadu, 600119, India  \n* corresponding author: ([p.naveen@mmu.edu.my](p.naveen@mmu.edu.my); ORCiD: 0000-0003-4601-9770)  \nAbstract-The COVID-19 pandemic has placed enormous strain on providing health care services internationally while reinforcing the argument for the need to strengthen forecasting techniques. Existing forecasting methods have drawbacks, especially in determining the long-term consequences of the pandemic and understanding its broad reach across various locations and populations. This project proposes an evaluation of machine learning (ML) models with the aim of improving predictions, particularly the accuracy in long-term forecasting, of subsequent trends ofthe COVID-19 pandemic. A systematic review highlights previous forecasting attempts as a reference for the approach. This project emphasizes extensive data collection, model formulation and testing to develop a strong prediction framework. The models considered for evaluation are Support Vector Regression (SVR), seasonal autoregressive integrated moving average (SARIMA), and artificial neural networks (ANN), which have overcome some of the deficiencies of epidemiological forecasting methods to date. The aim is to provide public health representatives with more rigorous forecasts, which could enhance planning and response measures and protect health and safety. Our findings show that the ANN model is superior, with high accuracy and comprehensive performance, confirming its broader use in various predictive applications. The Root Mean Square Error (RMSE) of prediction error was also relatively modest (R-square values were nearly 1) .  \nKeywords— COVID-19, Machine Learning, Support Vector Regression, Seasonal Autoregressive Integrated Moving Average, Artificial Neural Networks  \nReceived:12 November 2024; Accepted: 28 February 2025; Published: 16 June 2025 This is an open access article under the CC BY-NC-ND 4.0 license.  \n1. INTRODUCTION  \nThe ongoing crisis caused by the COVID-19 pandemic has affected the worldwide economy as well as the health care system across various countries. Starting in 2020, the outbreak of the virus in different parts of the world and its consequences on the regions have indicated an acute need for innovative strategies to determine how the breathing monster moves and how to tame it. To better prepare the public health system, let alone avert the risks posed by the virus, having an accurate projection of the virus outbreaks is essential.  \nHistorical data and several mathematical models have always been the basis for forecasting infectious diseases, with Epidemiological models leading the way [1], [2], [3] . Such models are predominantly based on past experiences, which create an outlook for new outbreaks. The models incorporate statistical and mathematical techniques to model the disease transmission process based on various ratios like infection rate, recovery rate and total death accounts. It is, however, worth noting that COVID-19 has unique features that defy most, if not all, of the assumptions of these  \ntraditional models, like the degree to which COVID-19 varies in versatility, degree of transmittance, and the marketing and social factors. Such an unprecedented circumstance calls for an apparent change in the existing forecasting tools utilized to reflect the current circumstances surrounding the pandemic.  \nThe weaknesses of the public health institutions have come to the front due to the COVID-19 pandemic. This scenario has reinforced the need for better computational techniques to advance forecasting capab","cbCaimdoXU4RbDHJ","https://ap.wps.com/l/cbCaimdoXU4RbDHJ","pdf",940035,6,1,13,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"Which machine learning models are evaluated for COVID-19 prediction?\",\"answer\":\"The study evaluates Support Vector Regression (SVR), SARIMA (seasonal autoregressive integrated moving average), and artificial neural networks (ANN).\"},{\"question\":\"What do the results indicate about ANN performance?\",\"answer\":\"The findings show the ANN model is superior, delivering high accuracy and comprehensive performance compared with the other evaluated models.\"}]","Performance Evaluation on COVID-19 Prediction using Machine Learning Models - read online free | PDF",1785904479,33,{"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":84,"head_meta":86,"extra_data":88,"updated_unix":29},"performance-evaluation-on-covid-19-prediction-using-machine-learning-models-read-online-free","",{"@graph":37,"@context":83},[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/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/performance-evaluation-on-covid-19-prediction-using-machine-learning-models-read-online-free/126328/",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","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79],{"name":74,"@type":75,"acceptedAnswer":76},"Which machine learning models are evaluated for COVID-19 prediction?","Question",{"text":77,"@type":78},"The study evaluates Support Vector Regression (SVR), SARIMA (seasonal autoregressive integrated moving average), and artificial neural networks (ANN).","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What do the results indicate about ANN performance?",{"text":82,"@type":78},"The findings show the ANN model is superior, delivering high accuracy and comprehensive performance compared with the other evaluated models.","https://schema.org",{"og:url":53,"og:type":85,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":87,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":90},[91,95,99,103,108,112,117,120,125,128,132],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},"Technology",50,"technology",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":118,"slug":119},30,"research-report",{"id":121,"doc_module":4,"doc_module_name":47,"category_name":122,"show_sort_weight":123,"slug":124},9,"Religion & Spirituality",20,"religion-spirituality",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":123,"slug":127},"World Cup","world-cup",{"id":129,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":129,"slug":131},10,"Lifestyle","lifestyle",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":104,"slug":135},19,"General","general"]