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Title: Harnessing the Power of Vocal Signals in COVID-19 Detection Utilizing Machine Learning
The global COVID-19 pandemic has strained healthcare systems and highlighted the need for accessible and efficient diagnostic methods. Traditional diagnostic tools, such as nasal swabs and biosensors, while accurate, pose significant logistical challenges and high costs, limiting their scalability. This paper explores an alternative, non-invasive approach to COVID-19 detection using machine learning algorithms to analyze vocal patterns, particularly cough and breathing sounds. Leveraging a publicly available dataset, we developed machine learning models capable of classifying audio samples as COVID-19 positive or negative. Our models achieve an AUC of up to 85% and an F1- score of 81%, demonstrating the potential of machine learning in enabling rapid, cost-effective COVID-19 diagnosis. These findings suggest that audio-based diagnostics could be a practical and scalable solution, particularly in resource-limited settings where traditional methods are less feasible.  more » « less
Award ID(s):
2334391
PAR ID:
10570734
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3503-6248-0
Page Range / eLocation ID:
4564 to 4570
Subject(s) / Keyword(s):
Coronavirus COVID-19 machine learning coughs breathing vocal signal analysis COVID-19 detection
Format(s):
Medium: X
Location:
Washington, DC, USA
Sponsoring Org:
National Science Foundation
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