"SOS Signal" in Breathing Sound - Rapid COVID-19 Diagnosis Based on Machine Learning

Abstract

Abstract—The severe acute respiratory syndrome coronavirus 2 is a novel type of coronavirus that causes COVID-19. The COVID-19 virus has recently infected more than 590 million individuals, resulting in a global pandemic. Traditional diagnosis methods are no longer effective due to the exponential rise in infection rates. Quick and accurate COVID-19 diagnosis is made possible by machine learning (ML), which also assuages the burden on healthcare systems. After the effective utilization of Cough Audio Signal Classification in diagnosing a number of respiratory illnesses, there has been significant interest in using ML to enable universal COVID-19 screening. The purpose of the current study is to determine people's COVID-19 status through machine learning algorithms. We have developed a Random Forest based model and achieved an accuracy of 0.873 on the COUGHVID dataset, demonstrates the potential of using audio signals as a cheap, accessible, and accurate COVID-19 screening tool.