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Current Respiratory Medicine Reviews

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ISSN (Print): 1573-398X
ISSN (Online): 1875-6387

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Research Article

Forecasting of Covid-19 Cases Using Machine Learning Approach

Author(s): Sachin Kumar and Karan Veer*

Volume 16, Issue 4, 2020

Page: [240 - 245] Pages: 6

DOI: 10.2174/1573398X17666210129131009

Price: $65

Abstract

Aims: The objective of this research is to predict the covid-19 cases in India based on the machine learning approaches.

Background: Covid-19, a respiratory disease caused by one of the coronavirus family members, has led to a pandemic situation worldwide in 2020. This virus was detected firstly in Wuhan city of China in December 2019. This viral disease has taken less than three months to spread across the globe.

Objective: In this paper, we proposed a regression model based on the Support Vector Machine (SVM) to forecast the number of deaths, the number of recovered cases, and total confirmed cases for the next 30 days.

Method: For prediction, the data was collected from Github and the ministry of India's health and family welfare from March 14, 2020, to December 3, 2020. The model has been designed in Python 3.6 in Anaconda to forecast the forecasting value of corona trends until September 21, 2020. The proposed methodology is based on the prediction of values using SVM based regression model with polynomial, linear, rbf kernel. The dataset has been divided into train and test datasets with 40% and 60% test size and verified with real data. The model performance parameters were evaluated as a mean square error, mean absolute error, and percentage accuracy.

Results and Conclusion: The results show that the polynomial model has obtained 95% above accuracy score, linear scored above 90%, and rbf scored above 85% in predicting cumulative death, conformed cases, and recovered cases.

Keywords: COVID-19, SVM model, polynomial, data analysis, rbf, machine learning.

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