A Comparison of Machine Learning Methods for best Accuracy COVID-19 Diagnosis Using Chest X-Ray Images

Abstract

Coronavirus (COVID-19) changed the view of people towards life in all the countries of the world in December 2019. The virus has made chaos that cannot be predicted. This problem requires using a variety of technologies to aid in the identification of COVID-19 patients and to control the disease spread. For suspected instances of COVID-19 disease, chest X-ray (CXR) imaging is a standard with fewer costs, but it does not need a COVID-19 examination approach without using technology to help for a suitable diagnosis. In response to this issue, a big dataset of CXR images was divided into four classes found on the website Kaggle. Dealing with large data of the images needs dataset reprocessing through choosing the optimal method for getting speed and best accuracy. Dataset reprocessing converts into gray level then adjust image intensity, resize and extract the best features then apply Machine Learning ML models. The use of different prediction models, ML algorithms, and their performances are calculated with evaluation on the dataset after reprocessing. Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and K-Nearest Neighbors (KNN) are models used to foretell the specialized who would be diagnosed with COVID-19 quickly by using CXR images classification. The KNN has revealed the best accuracy compared with the others such as GNB, DT, SGD, LR, and RF. Also, KNN has the best-weighted average for all parameters, which are precision, sensitivity, and F1-score compared with the other models.