Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Estimating COVID-19 Cases Using Machine Learning Regression Algorithms

Author(s): Vaishali Deshwal, Vimal Kumar, Rati Shukla and Vikash Yadav*

Volume 15, Issue 5, 2022

Published on: 10 August, 2022

Page: [390 - 400] Pages: 11

DOI: 10.2174/2352096515666220610155214

Price: $65

Abstract

Background: Coronavirus refers to a large group of RNA viruses that infects the respiratory tract in humans and also causes diseases in birds and mammals. SARS-CoV-2 gives rise to the infectious disease “COVID-19”. In March 2020, coronavirus was declared a pandemic by the WHO. The transmission rate of COVID-19 has been increasing rapidly; thus, it becomes indispensable to estimate the number of confirmed infected cases in the future.

Objective: The study aims to forecast coronavirus cases using three ML algorithms, viz., support vector regression (SVR), polynomial regression (PR), and Bayesian ridge regression (BRR).

Methods: There are several ML algorithms like decision tree, K-nearest neighbor algorithm, Random forest, neural networks, and Naïve Bayes, but we have chosen PR, SVR, and BRR as they have many advantages in comparison to other algorithms. SVM is a widely used supervised ML algorithm developed by Vapnik and Cortes in 1990. It is used for both classification and regression. PR is known as a particular case of Multiple Linear Regression in Machine Learning. It models the relationship between an independent and dependent variable as nth degree polynomial.

Results: In this study, we have predicted the number of infected confirmed cases using three ML algorithms, viz. SVR, PR, and BRR. We have assumed that there are no precautionary measures in place.

Conclusion: In this paper, predictions are made for the upcoming number of infected confirmed cases by analyzing datasets containing information about the day-wise past confirmed cases using ML models (SVR, PR and BRR). According to this paper, as compared to SVR and PR, BRR performed far better in the future forecasting of the infected confirmed cases owing to coronavirus.

Keywords: Prediction, coronavirus, COVID-19, support vector regression, polynomial regression, bayesian ridge regression.

« Previous Next »
Graphical Abstract

[1]
S.S. Arun, and G.N. Iyer, "On the analysis of COVID19-Novel corona viral disease pandemic spread data using machine learning techniques", In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), 13-15 May, 2020, Madurai, India, 2020, pp. 1222-1227.
[http://dx.doi.org/10.1109/ICICCS48265.2020.9121027]
[2]
D. Vaishali, and K. Vimal, "Study of Coronavirus disease (COVID-19) outbreak in India", Open Nurs. J., vol. 15, no. 1, pp. 1-5, 2021.
[3]
Z. Song, Y. Xu, L. Bao, L. Zhang, P. Yu, Y. Qu, H. Zhu, W. Zhao, Y. Han, and C. Qin, "From SARS to MERS, thrusting coronaviruses into the spotlight", Viruses, vol. 11, no. 1, p. 59, 2019.
[http://dx.doi.org/10.3390/v11010059]
[4]
G. Mansi, K. Vimal, and Y. Vikash, "Proposed framework for dealing Covid-19 pandemic using block chain technology", J. Sci. Ind. Res., vol. 80, no. 3, pp. 270-275, 2021.
[5]
F.E. Harrell Jr, K.L. Lee, D.B. Matchar, and T.A. Reichert, "Regression models for prognostic prediction: Advantages, problems, and suggested solutions", Cancer Treat. Rep., vol. 69, no. 10, pp. 1071-1077, 1985.
[PMID: 4042087]
[6]
D. Parbat, and M. Chakraborty, "A python based support vector regression model for prediction of COVID19 cases in India", Chaos Solitons Fractals, vol. 138, p. 109942, 2020.
[http://dx.doi.org/10.1016/j.chaos.2020.109942] [PMID: 32834576]
[7]
M. Ekum, and A. Ogunsanya, "Application of hierarchical polynomial regression models to predict transmission of COVID-19 at global level", Int. J. Clin. Biostat. Biom., vol. 6, no. 1, p. 27, 2020.
[8]
M.A. Roudbar, M. Momen, S.F. Mousavi, S.S. Ardestani, F.B. Lopes, D. Gianola, and H. Khatib, "Genome-wide methylation prediction of biological age using reproducing kernel Hilbert spaces and Bayesian ridge regressions", bioRxiv, 2020.
[http://dx.doi.org/10.1101/2020.08.25.266924]
[9]
W. Xu, X. Liu, F. Leng, and W. Li, "Blood-based multi-tissue gene expression inference with Bayesian ridge regression", Bioinformatics, vol. 36, no. 12, pp. 3788-3794, 2020.
[http://dx.doi.org/10.1093/bioinformatics/btaa239] [PMID: 32277818]
[10]
F. Rustam, A.A. Reshi, A. Mehmood, S. Ullah, B-W. On, W. Aslam, and G.S. Choi, "COVID-19 future forecasting using supervised machine learning models", IEEE Access, vol. 8, pp. 101489-101499, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.2997311]
[11]
G. Pooja, K. Vimal, and Y. Vikash, "Student’s perception towards mobile learning using internet enabled mobile devices during COVID-19", EAI Endorsed Trans. Ind. Netw. Intell. Syst., vol. 8, no. 29, pp. 1-17, 2021.
[12]
Johns Hopkins University Data Repository, Cssegisanddata.. Available from: https://github.com/CSSEGISandData (Accessed on May 25, 2022).
[13]
C.J. Willmott, and K. Matsuura, "Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in assessing average model performance", Clim. Res., vol. 30, no. 1, pp. 79-82, 2005.
[http://dx.doi.org/10.3354/cr030079]
[14]
"Python libraries". Available from: https://www.mygreatlearning.com/blog/open-source-python-libraries/ (Accessed on May 25, 2022).
[15]
D. Baud, X. Qi, K. Nielsen-Saines, D. Musso, L. Pomar, and G. Favre, "Real estimates of mortality following COVID-19 infection", Lancet Infect. Dis., vol. 20, no. 7, p. 773, 2020.
[http://dx.doi.org/10.1016/S1473-3099(20)30195-X] [PMID: 32171390]
[16]
A. Basu, "Estimating the infection fatality rate among symptomatic COVID-19 cases in the United States: Study estimates the COVID-19 infection fatality rate at the US county level", Health Aff., vol. 39, no. 7, pp. 1229-1236, 2020.
[http://dx.doi.org/10.1377/hlthaff.2020.00455]
[17]
A. Deniz, H.E. Kiziloz, E. Sevinc, and T. Dokeroglu, "Predicting the severity of COVID‐19 patients using a multi‐threaded evolutionary feature selection algorithm", Expert Syst., 2022, p. e12949.
[http://dx.doi.org/10.1111/exsy.12949]
[18]
T.A. Mellan, H.H. Hoeltgebaum, S. Mishra, C. Whittaker, R.P. Schnekenberg, and A. Gandy, "Report 21: Estimating COVID-19 cases and reproduction number in Brazil", 2020.

Rights & Permissions Print Cite
Article Metrics
1
© 2024 Bentham Science Publishers | Privacy Policy