Kukuseva, Maja and Martinovska Bande, Cveta and Stojkovic, Natasa and Bikov, Dusan (2025) Machine Learning Models for Prediction of COVID-19 Infection in North Macedonia. TEM Journal, 14 (1). pp. 160-168. ISSN 2217-8309 / 2217-8333 (Online)
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Abstract
The COVID-19 pandemic, caused by the
SARS-CoV-2 virus, has emerged as one of the most
significant global crises of this century, with severe
health and socio-economic impacts worldwide. Existing
research has highlighted the critical role of
comorbidities in influencing COVID-19 outcomes, but
effective prediction models remain a challenge. This
study investigates the potential of machine learning
algorithms to predict the outcomes of COVID-19 based
on patients' comorbidities. The algorithms K-Nearest
Neighbors, Decision Tree, Logistic Regression, and
Random Forest are applied to an epidemiological
dataset comprising only positive COVID-19 cases,
obtained from the Public Health Institute of North
Macedonia. Additionally, two ensemble learning
techniques, XGBoost and RUSBoost, are used to
enhance prediction accuracy. The models achieved high
accuracy of 90% across the various algorithms. These
findings suggest that machine learning models can be an
effective tool for predicting COVID-19 outcomes,
especially when comorbidity data is available.
Item Type: | Article |
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Impact Factor Value: | 0.6 |
Subjects: | Natural sciences > Computer and information sciences |
Divisions: | Faculty of Computer Science |
Depositing User: | Maja Kukuseva |
Date Deposited: | 12 Mar 2025 08:01 |
Last Modified: | 12 Mar 2025 08:01 |
URI: | https://eprints.ugd.edu.mk/id/eprint/35790 |