The Performance Comparison of Machine Learning Models for COVID-19 Classification Based on Chest X-ray

Elvira Sukma Wahyuni

doi:10.14710/jbiomes.2022.v2i1.1-6

DOI: https://doi.org/10.14710/jbiomes.2022.v2i1.1-6

The Performance Comparison of Machine Learning Models for COVID-19 Classification Based on Chest X-ray

*Elvira Sukma Wahyuni

- Universitas Islam Indonesia, Indonesia

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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%.

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Keywords: COVID-19; Chest X-Ray; Machine Learning; Naive Bayes; Logistic Regression; K-NN; Decision Tree

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Section: Articles

Language : EN

In Vol 2, No 1 (2022)

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