@Article{cmc.2020.012045, AUTHOR = {Abdu Gumaei, Mabrook Al-Rakhami, Mohamad Mahmoud Al Rahhal, Fahad Raddah H. Albogamy, Eslam Al Maghayreh, Hussain AlSalman}, TITLE = {Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method}, JOURNAL = {Computers, Materials \& Continua}, VOLUME = {66}, YEAR = {2021}, NUMBER = {1}, PAGES = {315--329}, URL = {http://www.techscience.com/cmc/v66n1/40449}, ISSN = {1546-2226}, 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.}, DOI = {10.32604/cmc.2020.012045} }