Performance evaluation of classifiers for the COVID-19 symptom-based dataset using different feature selection methods

Abstract

Recently, a global pandemic with high fatality rate has been affecting many lives and it is caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) known as coronavirus disease COVID-19. Numerous studies have been published in the last three years identifying the symptoms in COVID-19 patients in various countries around the world. The typical signs of an infection may include experiencing a high temperature, difficulties with breathing, a dry cough, a sore throat, feeling breathless, fatigue, loss of smell and taste, and other respiratory issues [1,2].