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

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dc.contributor.authorZgheib, Rita
dc.contributor.authorKamalov, Firuz
dc.contributor.authorChahbandarian, Ghazar
dc.contributor.authorEl Labban, Osman
dc.date.accessioned2021-12-07T16:41:10Z
dc.date.available2021-12-07T16:41:10Z
dc.date.copyright© 2021
dc.date.issued2021
dc.descriptionThis work is not available in the CUD collection. The version of the scholarly record of this work is published in Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science (2021), available online at: https://doi.org/10.1007/978-3-030-84529-2_52en_US
dc.description.abstractGiven 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.citationZgheib, R., Kamalov, F., Chahbandarian, G., & Labban, O. E. (2021). Diagnosing COVID-19 on limited data: A comparative study of machine learning methods. In DS Huang, KH Jo, J. Li, V. Gribova & A. Hussain (Eds.), Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, Vol. 12837. (pp. 616–627). Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_52en_US
dc.identifier.isbn978-303084528-5
dc.identifier.issn03029743
dc.identifier.urihttp://hdl.handle.net/20.500.12519/476
dc.identifier.urihttps://doi.org/10.1007/978-3-030-84529-2_52
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relationAuthors 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.ispartofseriesIntelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science; Volume 12837
dc.rightsLicense to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holderCopyright : © 2021, Springer Nature Switzerland AG.
dc.rights.urihttps://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=462b0c83-abc2-42c8-9a1c-f9f9b9128c1c
dc.subjectCOVID-19 detectionen_US
dc.subjectGBDTen_US
dc.subjectNeural networken_US
dc.subjectRandom foresten_US
dc.subjectSVMen_US
dc.titleDiagnosing COVID-19 on Limited Data: A Comparative Study of Machine Learning Methodsen_US
dc.typeConference Paperen_US

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