The Performance Comparison of Machine Learning Models for COVID-19 Classification Based on Chest X-ray - Elvira Sukma Wahyuni (Journal of Biomedical Science and Bioengineering, 2022)
COVID-19 has become a pandemic spread to nearly all countries in the world. This virus has caused many deaths. Screening using a chest X-ray is an alternative to find out positive COVID-19 patients. Chest X-ray is advantageous because every hospital must have an X-ray device so that hospitals do not need additional equipment to detect COVID-19-positive patients. This study aims to compare the machine learning models of Naive Bayes, Decision Tree, K-Nearest Neighbor, and Logistic Regression to predict COVID-19 positive patients. The stages of the research carried out by this study are the Pre-process stage, feature extraction, and classification. The results showed that the Naïve Bayes classification method got the highest performance with an accuracy of 95.24%.