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Machine Learning-based Early Detection and Prognosis of the Covid-19 Pandemic

Journal of ICT Research and Applications (JICTRA) · Vol. 17 No. 2 (2023) · pp. 214–230

DOI
10.5614/itbj.ict.res.appl.2023.17.2.6
ISSN
2338-5499, 2337-5787
Published
2023-09-01
Publisher
DRI - ITB
Language
eng

Abstract

The outbreak of Covid-19 has caused a global health crisis, presenting numerous challenges to the healthcare system with its severe respiratory symptoms and variable presentation. Early and accurate diagnosis of the virus is critical in controlling its spread and reducing the burden on healthcare facilities. To address this issue and relieve the strain on the healthcare system, this paper proposes a machine learning-based approach for Covid-19 diagnosis. Four algorithms were used for analyzing early Covid-19 detection, i.e., logistic regression, random forest, decision tree, and naive Bayes, using a data set of basic symptoms such as fever, shortness of breath, etc. for predicting positive and negative Covid-19 cases. Furthermore, development of a web portal that provides information on global vaccine distribution and the most widely used vaccines by country along with Covid-19 predictions. Our evaluation results demonstrate that the decision tree model outperformed the other models, achieving an accuracy of 97.69%. This study provides a practical solution to the ongoing Covid-19 crisis through an improved diagnosis method and access to vaccination information.

Keywords

Collaboration network of the authors

3 authors · 3 co-authorship links

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  • Ramaiah Institute of Technology
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Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Machine Learning-based Early Detection and Prognosis of the Covid-19 Pandemic</dc:title>
	<dc:creator>Santhakumari, Ajitha</dc:creator>
	<dc:creator>Shilpa, R.</dc:creator>
	<dc:creator>Abdulwahab, Hudhaifa Mohammed</dc:creator>
	<dc:subject xml:lang="en-US">Covid-19</dc:subject>
	<dc:subject xml:lang="en-US">data preprocessing</dc:subject>
	<dc:subject xml:lang="en-US">healthcare</dc:subject>
	<dc:subject xml:lang="en-US">machine learning</dc:subject>
	<dc:subject xml:lang="en-US">predictive analysis</dc:subject>
	<dc:subject xml:lang="en-US">web tool</dc:subject>
	<dc:description xml:lang="en-US">The outbreak of Covid-19 has caused a global health crisis, presenting numerous challenges to the healthcare system with its severe respiratory symptoms and variable presentation. Early and accurate diagnosis of the virus is critical in controlling its spread and reducing the burden on healthcare facilities. To address this issue and relieve the strain on the healthcare system, this paper proposes a machine learning-based approach for Covid-19 diagnosis. Four algorithms were used for analyzing early Covid-19 detection, i.e., logistic regression, random forest, decision tree, and naive Bayes, using a data set of basic symptoms such as fever, shortness of breath, etc. for predicting positive and negative Covid-19 cases. Furthermore, development of a web portal that provides information on global vaccine distribution and the most widely used vaccines by country along with Covid-19 predictions. Our evaluation results demonstrate that the decision tree model outperformed the other models, achieving an accuracy of 97.69%. This study provides a practical solution to the ongoing Covid-19 crisis through an improved diagnosis method and access to vaccination information.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2023-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/17709</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2023.17.2.6</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 17 No. 2 (2023); 214-230</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2023.17.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/17709/6310</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Journal of ICT Research and Applications</dc:rights>
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