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. 2021;80(8):11943-11957.
doi: 10.1007/s11042-020-10340-7. Epub 2021 Jan 7.

Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms

Ibrahim Arpaci #  1 , Shigao Huang #  2 , Mostafa Al-Emran  3 , Mohammed N Al-Kabi  4 , Minfei Peng  5
Affiliations

Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms

Ibrahim Arpaci et al. Multimed Tools Appl. 2021.

Abstract

While the RT-PCR is the silver bullet test for confirming the COVID-19 infection, it is limited by the lack of reagents, time-consuming, and the need for specialized labs. As an alternative, most of the prior studies have focused on Chest CT images and Chest X-Ray images using deep learning algorithms. However, these two approaches cannot always be used for patients' screening due to the radiation doses, high costs, and the low number of available devices. Hence, there is a need for a less expensive and faster diagnostic model to identify the positive and negative cases of COVID-19. Therefore, this study develops six predictive models for COVID-19 diagnosis using six different classifiers (i.e., BayesNet, Logistic, IBk, CR, PART, and J48) based on 14 clinical features. This study retrospected 114 cases from the Taizhou hospital of Zhejiang Province in China. The results showed that the CR meta-classifier is the most accurate classifier for predicting the positive and negative COVID-19 cases with an accuracy of 84.21%. The results could help in the early diagnosis of COVID-19, specifically when the RT-PCR kits are not sufficient for testing the infection and assist countries, specifically the developing ones that suffer from the shortage of RT-PCR tests and specialized laboratories.

Keywords: COVID-19; Classification algorithms; Diagnosis; Machine learning; Novel coronavirus; Prediction.

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Conflict of interest statement

Conflict of interestthe authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Applications of machine learning algorithms on COVID-19
Fig. 2
Fig. 2
Data flow diagram
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Fig. 3
J48 Decision Tree
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Fig. 4
ROC curves for the CR
Fig. 5
Fig. 5
Cost/benefit analysis curves
See this image and copyright information in PMC

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