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

Zgheib, Rita
Kamalov, Firuz
Chahbandarian, Ghazar
El Labban, Osman
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Springer Science and Business Media Deutschland GmbH
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.
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
COVID-19 detection, GBDT, Neural network, Random forest, SVM
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