Diagnosing COVID-19 on Limited Data: A Comparative Study of Machine Learning Methods

dc.contributor.author Zgheib, Rita
dc.contributor.author Kamalov, Firuz
dc.contributor.author Chahbandarian, Ghazar
dc.contributor.author El Labban, Osman
dc.date.accessioned 2021-12-07T16:41:10Z
dc.date.available 2021-12-07T16:41:10Z
dc.date.copyright © 2021
dc.date.issued 2021
dc.description This conference paper is not available at CUD collection. The version of scholarly record of this article is published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2021), available online at: https://doi.org/10.1007/978-3-030-84529-2_52 en_US
dc.description.abstract Given the enormous impact of COVID-19, effective and early detection of the virus is a crucial research question. In this paper, we compare the effectiveness of several machine learning algorithms in detecting COVID-19 virus based on patient’s age, gender, and nationality. The results of the experiments show that neural networks, support vector machines, and gradient boosting decision tree models achieve an 89% accuracy, and the random forest model produces an 87% accuracy in the identification of the COVID-19 cases. © 2021, Springer Nature Switzerland AG. en_US
dc.identifier.citation Zgheib, R., Kamalov, F., Chahbandarian, G., & Labban, O. E. (2021). Diagnosing COVID-19 on limited data: A comparative study of machine learning methods. In: Huang DS., Jo KH., Li J., Gribova V., Hussain A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_52 en_US
dc.identifier.isbn 978-303084528-5
dc.identifier.issn 03029743
dc.identifier.uri http://hdl.handle.net/20.500.12519/476
dc.identifier.uri https://doi.org/10.1007/978-3-030-84529-2_52
dc.language.iso en en_US
dc.publisher Springer Science and Business Media Deutschland GmbH en_US
dc.relation Authors Affiliations : Zgheib, R., School of Computer Engineering, Canadian University Dubai, Dubai, United Arab Emirates; Kamalov, F., School of Computer Engineering, Canadian University Dubai, Dubai, United Arab Emirates; Chahbandarian, G., Institut de Recherche en Informatique de Toulouse, Toulouse, France; Labban, O.E., Head of Family Medicine Department, Al Zahra Hospital, Dubai, United Arab Emirates
dc.relation.ispartofseries Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics);Volume 12837
dc.rights License to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holder Copyright : © 2021, Springer Nature Switzerland AG.
dc.rights.uri https://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=240b5b77-f7fe-404e-9fa5-1740dec7fa2c
dc.subject COVID-19 detection en_US
dc.subject GBDT en_US
dc.subject Neural network en_US
dc.subject Random forest en_US
dc.subject SVM en_US
dc.title Diagnosing COVID-19 on Limited Data: A Comparative Study of Machine Learning Methods en_US
dc.type Conference Paper en_US
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