Open Access
ARTICLE
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:
Computers, Materials & Continua 2021, 66(1), 315-329. https://doi.org/10.32604/cmc.2020.012045
Received 11 June 2020; Accepted 22 July 2020; Issue published 30 October 2020
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.
Keywords
Cite This Article
APA Style
Gumaei, A., Al-Rakhami, M., Rahhal, M.M.A., Albogamy, F.R.H., Maghayreh, E.A. et al. (2021). Prediction of COVID-19 confirmed cases using gradient boosting regression method. Computers, Materials & Continua, 66(1), 315-329. https://doi.org/10.32604/cmc.2020.012045
Vancouver Style
Gumaei A, Al-Rakhami M, Rahhal MMA, Albogamy FRH, Maghayreh EA, AlSalman H. Prediction of COVID-19 confirmed cases using gradient boosting regression method. Comput Mater Contin. 2021;66(1):315-329 https://doi.org/10.32604/cmc.2020.012045
IEEE Style
A. Gumaei, M. Al-Rakhami, M.M.A. Rahhal, F.R.H. Albogamy, E.A. Maghayreh, and H. AlSalman "Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method," Comput. Mater. Contin., vol. 66, no. 1, pp. 315-329. 2021. https://doi.org/10.32604/cmc.2020.012045
Citations