Citation

Sayeed, Md. Shohel and Hishamuddin, Siti Najihah and Ong, Thian Song (2024) Covid-19 forecasting model based on machine learning approaches: a review. Bulletin of Electrical Engineering and Informatics, 13 (6). pp. 4335-4345. ISSN 2089-3191

Text Covid-19 forecasting model based on machine learning approaches_ a review _ Sayeed _ Bulletin of Electrical Engineering and Informatics.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

As coronavirus disease (Covid-19) it is a contagious disease that is spread by the SARS-CoV-2 virus, one of the most common causes of disease in humans. The disease was initially discovered in Wuhan, China, in 2019, and has now spread throughout the world, including Malaysia. A large number of people have lost their life partners and families because of this disease. Thus, in order for us to stop this epidemic spread, we have to implement social distance. The Covid-19 infection displays this type of behavior, which necessitates the development of mathematical and predictive modeling techniques capable of predicting possible disease patterns or trends, in order to assist the government and health authorities in predicting and preparing for potential outbreaks. The purpose of this paper is to provide an in-depth critique and analysis of the machine-learning approaches that have been implemented by researchers to predict Covid-19, based on existing research. As a result, future researchers will be able to use this paper as a valuable resource for their research related to the Covid-19 forecasting mode

Item Type:	Article
Uncontrolled Keywords:	Analysis Covid-19,Forecasting, Machine learning, Predictive modeling
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics R Medicine > RA Public aspects of medicine
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Nov 2024 01:10
Last Modified:	04 Nov 2024 01:10
URII:	http://shdl.mmu.edu.my/id/eprint/13080

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