@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}
}