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.
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The authors would like to thank Al Zahra hospital for their cooperation, support, and sharing knowledge.
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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
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DOI: https://doi.org/10.1007/978-3-030-84529-2_52
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