Machine Learning Models for Prediction ofCOVID-19 Infection in North Macedonia

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.