Diagnosing COVID-19 on Limited Data: A Comparative Study of Machine Learning Methods
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media Deutschland GmbH
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.
Description
item.page.type
Conference Paper
item.page.format
Keywords
COVID-19 detection, GBDT, Neural network, Random forest, SVM
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 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_52