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Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method

Abdu Gumaei1,2,*, Mabrook Al-Rakhami1, Mohamad Mahmoud Al Rahhal3, Fahad Raddah H. Albogamy3, Eslam Al Maghayreh3, Hussain AlSalman1

1 College of Computer and Information Sciences, King Saud University, Riyadh, 11362, Saudi Arabia
2 Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, Yemen
3 College of Applied Computer Sciences, King Saud University, Riyadh, 11362, Saudi Arabia

* Corresponding Author: Abdu Gumaei. Email: email

Computers, Materials & Continua 2021, 66(1), 315-329. https://doi.org/10.32604/cmc.2020.012045

Abstract

The fast spread of coronavirus disease (COVID-19) caused by SARSCoV-2 has become a pandemic and a serious threat to the world. As of May 30, 2020, this disease had infected more than 6 million people globally, with hundreds of thousands of deaths. Therefore, there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems. This study uses gradient boosting regression (GBR) to build a trained model to predict the daily total confirmed cases of COVID-19. The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners. Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22, 2020, to May 30, 2020. The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method. The results reveal that the GBR model achieves 0.00686 root mean square error, the lowest among several comparative models.

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Cite This Article

A. Gumaei, M. Al-Rakhami, M. Mahmoud Al Rahhal, F. Raddah H. Albogamy, E. Al Maghayreh et al., "Prediction of covid-19 confirmed cases using gradient boosting regression method," Computers, Materials & Continua, vol. 66, no.1, pp. 315–329, 2021.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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