Diagnosing COVID-19 on Limited Data: A Comparative Study of Machine Learning Methods
Date
2021
item.page.datecreated
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
This 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_52
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