BibTex Citation Data :
@article{JBIOMES14326, author = {Elvira Wahyuni}, title = {The Performance Comparison of Machine Learning Models for COVID-19 Classification Based on Chest X-ray}, journal = {Journal of Biomedical Science and Bioengineering}, volume = {2}, number = {1}, year = {2022}, keywords = {COVID-19; Chest X-Ray; Machine Learning; Naive Bayes; Logistic Regression; K-NN; Decision Tree}, abstract = {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%.}, issn = {2776-4052}, pages = {1--6} doi = {10.14710/jbiomes.2022.v2i1.1-6}, url = {https://ejournal2.undip.ac.id/index.php/jbiomes/article/view/14326} }
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