Selected article for: "early detection and effective early detection"

Author: Zgheib, R.; Kamalov, F.; Chahbandarian, G.; Labban, O. E.
Title: Diagnosing COVID-19 on Limited Data: A Comparative Study of Machine Learning Methods
  • Cord-id: 3a4ppp1n
  • Document date: 2021_1_1
  • ID: 3a4ppp1n
    Snippet: 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 identificatio
    Document: 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.

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