ANALYSIS OF COVID-19 DATA AND PREDICTING FUTURE CORONAVIRUS CASES BY USING MACHINE LEARNING ALGORITHMS
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Date
2023
Authors
Zhaksybay, Uldana
Journal Title
Journal ISSN
Volume Title
Publisher
School of Engineering and Digital Sciences
Abstract
Background: The Covid-19 pandemic has posed significant challenges to healthcare
systems worldwide. Effective strategies to manage the pandemic require accurate and
timely forecasting of the spread of the virus. Machine learning (ML) algorithms offer
a promising approach for predicting the number of Covid-19 cases.
Objectives: This thesis work aims to analyze the Coronavirus data, and the number
of cases and predict the future behavior of Covid-19 in Kazakhstan which helps
to make key decisions related to the virus and prevent the country from the global
economic crisis.
Methods: The study utilized publicly available data sources to create a comprehensive
Covid-19 dataset. The dataset included daily counts of confirmed Covid-19 cases,
deaths, recoveries, and tests across multiple countries and regions worldwide. This
work used four ML algorithms in our study, including a decision tree, random forest,
linear regression (LR), and polynomial regression. Evaluation of the performance of
the models based on r2 score, MAE, MSE.
Results: Results showed that all four ML algorithms produced reasonably accurate
predictions of Covid-19 cases. The random forest and decision tree algorithms outperformed
the other models, with an accuracy rate of over 85% and 90% respectively.
The linear and polynomial regression models had accuracy rates of approximately
over 75%.
Conclusion: In conclusion, this study demonstrates the potential of ML algorithms
for predicting the number of Covid-19 cases. Findings suggest that the random forest
algorithm is the most effective in forecasting Covid-19 cases. The results of this
study may help inform policymakers and healthcare professionals in developing effective
strategies to manage the Covid-19 pandemic.
Description
Keywords
Type of access: Open Access, Covid-19, coronavirus, Machine learning algorithms
Citation
Zhaksybay, U. (2023). Analysis of Covid-19 data and predicting future coronavirus cases by using Machine learning algorithms. School of Engineering and Digital Sciences