{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T16:10:21Z","timestamp":1787069421432,"version":"build-2736575974"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Early diagnosis is crucial to prevent the development of a disease that may cause danger to human lives. COVID-19, which is a contagious disease that has mutated into several variants, has become a global pandemic that demands to be diagnosed as soon as possible. With the use of technology, available information concerning COVID-19 increases each day, and extracting useful information from massive data can be done through data mining. In this study, authors utilized several supervised machine learning algorithms in building a model to analyze and predict the presence of COVID-19 using the COVID-19 Symptoms and Presence dataset from Kaggle. J48 Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Na\u00efve Bayes algorithms were applied through WEKA machine learning software. Each model\u2019s performance was evaluated using 10-fold cross validation and compared according to major accuracy measures, correctly or incorrectly classified instances, kappa, mean absolute error, and time taken to build the model. The results show that Support Vector Machine using Pearson VII universal kernel outweighs other algorithms by attaining 98.81% accuracy and a mean absolute error of 0.012.<\/jats:p>","DOI":"10.3390\/a14070201","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T10:03:19Z","timestamp":1625047399000},"page":"201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["COVID-19 Prediction Applying Supervised Machine Learning Algorithms with Comparative Analysis Using WEKA"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6905-8727","authenticated-orcid":false,"given":"Charlyn Nayve","family":"Villavicencio","sequence":"first","affiliation":[{"name":"Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan"},{"name":"College of Information and Communications Technology, Bulacan State University, Bulacan 3000, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2738-4932","authenticated-orcid":false,"given":"Julio Jerison Escudero","family":"Macrohon","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xavier Alphonse","family":"Inbaraj","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0026-8333","authenticated-orcid":false,"given":"Jyh-Horng","family":"Jeng","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jer-Guang","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, I-Shou University, Kaohsiung City 84001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO) (2021, May 23). 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