Classification of COVID-19 Disease Based on Extra Tree Features Selection

Asraa M.  Mohammad; Hussien  Attia; Yossra H.  Ali

doi:10.24996/ijs.2024.65.11.37

Authors

Asraa M. Mohammad Department of Computer Sciences, College of Science for Women, University of Babylon, Babylon, Iraq https://orcid.org/0009-0004-8231-1394
Hussien Attia Department of Computer Sciences, College of Science for Women, University of Babylon, Babylon, Iraq
Yossra H. Ali Department of Computer Sciences, University of Technology, Baghdad, Iraq

DOI:

https://doi.org/10.24996/ijs.2024.65.11.37

Keywords:

COVID-19, Classification, Machine Learning, Gray-Level Co-occurrence Matrix GLCM, Feature Selection

Abstract

The World Health Organization (WHO) has classified coronavirus as a global health emergency. Chest X-rays have been proven to be beneficial in both diagnosing and monitoring various lung diseases, including COVID-19. In this study, a COVID-19 disease detection framework is provided based on the methods used in machine learning and the Extra Tree algorithm to reduce the features extracted from the images. Using the Gray-Level Co-occurrence Matrix (GLCM) algorithm and the Extra Tree algorithm to choose the features of the work, a set of features is extracted from the images. This set of features is then put into the XGBoost algorithm to be classified. The proposed system was evaluated using two different sets of databases: the large database with 9544 images and the small database with 800 images. All image sizes were set to 300 x 300 pixels. The proposed system achieved a classification accuracy score of 90.04% using the large data set and 99.37% using the small set.