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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.14, No.3, Jun. 2022

A Comprehensive Review of Machine Learning Techniques for Predicting the Outbreak of Covid-19 Cases

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Author(s)

Arpita Santra, Ambar Dutta

Index Terms

Forecasting;Epidemic Covid-19;Machine Learning;Models;Performance Analysis

Abstract

At present, the whole world is experiencing a huge disturbance in social, economic, and political levels which may mostly attributed to sudden outbreak of Covid-19. The World Health Organization (WHO) declared it as Public Health crisis and global pandemic. Researchers across the globe have already proposed different outbreak models to impose various control measures fight against the novel corona virus. In order to overcome various challenges for the prediction of Covid-19 outbreaks, different mathematical and statistical approaches have been recommended by the researchers. The approaches used machine learning and deep learning based techniques which are capable of prediction of hidden patterns from large and complex datasets. The purpose of the present paper is to study different machine learning and deep learning based techniques used to identify and predict the pattern and performs some comparative analysis on the techniques. This paper contains a detailed summary of 40 paper based on this issue along with the use of method they applied to obtain the purpose. After the review it has been found that no model is fully capable of predicting it with accuracy. So, a hybrid model with better training should be employed for better result. This paper also studies different performance measures that researchers have used to show the efficiency of their proposed model.

Cite This Paper

Arpita Santra, Ambar Dutta, "A Comprehensive Review of Machine Learning Techniques for Predicting the Outbreak of Covid-19 Cases", International Journal of Intelligent Systems and Applications(IJISA), Vol.14, No.3, pp.40-53, 2022. DOI: 10.5815/ijisa.2022.03.04

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