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

作     者：Abdu Gumaei Mabrook Al-Rakhami Mohamad Mahmoud Al Rahhal Fahad Raddah H.Albogamy Eslam Al Maghayreh Hussain AlSalman 

作者机构：College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia Computer Science DepartmentFaculty of Applied ScienceTaiz UniversityTaizYemen College of Applied Computer SciencesKing Saud UniversityRiyadh11362Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第66卷第1期

页      面：315-329页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 1003[医学-口腔医学] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 10[医学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The financial support provided from the Deanship of Scientific Research at King SaudUniversity Research group No.RG-1441-502. 

主　　题：COVID-19 coronavirus disease SARS-CoV-2 machine learning gradient boosting regression(GBR)method 

摘      要：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.