{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:18:16Z","timestamp":1753881496597,"version":"3.41.2"},"reference-count":39,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T00:00:00Z","timestamp":1620432000000},"content-version":"vor","delay-in-days":127,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The coronavirus disease 2019 (Covid\u201019) pandemic has affected most countries of the world. The detection of Covid\u201019 positive cases is an important step to fight the pandemic and save human lives. The polymerase chain reaction test is the most used method to detect Covid\u201019 positive cases. Various molecular methods and serological methods have also been explored to detect Covid\u201019 positive cases. Machine learning algorithms have been applied to various kinds of datasets to predict Covid\u201019 positive cases. The machine learning algorithms were applied on a Covid\u201019 dataset based on commonly taken laboratory tests to predict Covid\u201019 positive cases. These types of datasets are easy to collect. The paper investigates the application of decision tree ensembles which are accurate and robust to the selection of parameters. As there is an imbalance between the number of positive cases and the number of negative cases, decision tree ensembles developed for imbalanced datasets are applied. F\u2010measure, precision, recall, area under the precision\u2010recall curve, and area under the receiver operating characteristic curve are used to compare different decision tree ensembles. Different performance measures suggest that decision tree ensembles developed for imbalanced datasets perform better. Results also suggest that including age as a variable can improve the performance of various ensembles of decision trees.<\/jats:p>","DOI":"10.1155\/2021\/5550344","type":"journal-article","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T17:05:14Z","timestamp":1620493514000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Decision Tree Ensembles to Predict Coronavirus Disease 2019 Infection: A Comparative Study"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9062-8170","authenticated-orcid":false,"given":"Amir","family":"Ahmad","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5114-0412","authenticated-orcid":false,"given":"Ourooj","family":"Safi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4339-3791","authenticated-orcid":false,"given":"Sharaf","family":"Malebary","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4422-8678","authenticated-orcid":false,"given":"Sami","family":"Alesawi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6617-1051","authenticated-orcid":false,"given":"Entisar","family":"Alkayal","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,8]]},"reference":[{"volume-title":"Naming the Coronavirus Disease (Covid-19) and the Virus that Causes it","author":"World Health Organization","key":"e_1_2_9_1_2"},{"volume-title":"WHO Director-General\u2019s Opening Remarks at the Media Briefing on Covid-19","author":"World Health Organization","key":"e_1_2_9_2_2"},{"volume-title":"Covid-19 Dashboard by the Center for Systems Science and Engineering (Csse) at","author":"Johns Hopkins University","key":"e_1_2_9_3_2"},{"volume-title":"Coronavirus Disease 2019 (Covid-19)","author":"World Health Organization","key":"e_1_2_9_4_2"},{"volume-title":"Coronavirus Disease 2019 Testing Basics","author":"World Health Organization","key":"e_1_2_9_5_2"},{"volume-title":"Pattern Recognition and Machine Learning","year":"2008","author":"Bishop C. 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