The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges

被引：51

作者：

Ahmad, Amir ^{[1
]}

Garhwal, Sunita ^{[2
]}

Ray, Santosh Kumar ^{[3
]}

Kumar, Gagan ^{[4
]}

Malebary, Sharaf Jameel ^{[5
]}

Barukab, Omar Mohammed ^{[5
]}

机构：

[1] United Arab Emirates Univ, Coll Informat Technol, Al Ain, U Arab Emirates

[2] Thapar Univ, Dept Comp Sci & Engn, Patiala, Punjab, India

[3] Khawarizmi Int Coll, Dept Informat Technol, Al Ain, U Arab Emirates

[4] Indian Inst Technol Guwahati, Dept Phys, Gauhati 781039, Assam, India

[5] King Abdulaziz Univ, Fac Comp & Informat Technol, POB 411, Jeddah 21911, Saudi Arabia

来源：

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING | 2021年 / 28卷 / 04期

关键词：

PREDICTION;

D O I：

10.1007/s11831-020-09472-8

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.

引用

下载

页码：2645 / 2653

页数：9

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