Vol.14, No.1, February 2025.                                                                                                                                                                          ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

Machine Learning Models for Prediction of COVID-19 Infection in North Macedonia

Maja Kukusheva Paneva, Cveta Martinovska Bande, Natasha Stojkovikj, Dushan Bikov

© 2025 Natasha Stojkovikj, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 14, Issue 1, Pages 160-168, ISSN 2217-8309, DOI: 10.18421/TEM141-15, February 2025.

Received: 09 July 2024.

Revised:  23 October 2024.
Accepted: 24 December 2024.
Published: 27 February 2025.

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.

Keywords – Machine learning, classification, ensemble methods, COVID-19 dataset

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