TY - JOUR
T1 - Prediction of COVID-19 confirmed cases using gradient boosting regression method
AU - Gumaei, Abdu
AU - Al-Rakhami, Mabrook
AU - Al Rahhal, Mohamad Mahmoud
AU - Albogamy, Fahad Raddah H.
AU - Al Maghayreh, Eslam
AU - AlSalman, Hussain
N1 - Publisher Copyright:
© 2020 Tech Science Press. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - COVID-19
KW - Coronavirus disease
KW - Gradient boosting regression (GBR) method
KW - Machine learning
KW - SARS-CoV-2
UR - http://www.scopus.com/inward/record.url?scp=85096501736&partnerID=8YFLogxK
U2 - 10.32604/cmc.2020.012045
DO - 10.32604/cmc.2020.012045
M3 - Article
AN - SCOPUS:85096501736
SN - 1546-2218
VL - 66
SP - 315
EP - 329
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
IS - 1
ER -
