Authors :
Gayathri B R; Karthika R; Sneha A; S Varsha
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/y7dzaunb
DOI :
https://doi.org/10.5281/zenodo.8041580
Abstract :
The COVID-19 pandemic has caused significant
disruptions to global health, so-ciety, and the economy.
Rapid and accurate detection of COVID-19 is crucial in
minimizing community outbreaks and controlling the
spread of the virus. This study proposes an audio-based
digital testing method for COVID-19, eliminat-ing the need
for patients to travel to testing laboratories. By analyzing
cough noises using machine learning and deep learning
techniques, the presence of COVID-19 can be detected and
classified. The study evaluates multiple ma-chine learning
models on the Coughvid dataset and assesses their
performance in terms of accuracy. The results reveal that
gradient boost achieves the highest accuracy of 88.82%,
followed closely by Xgboost with an accuracy of 88.53%.
Decision tree-based models, such as the Voting Classifier
and Adaboost, also exhibit strong performance with
accuracies above 88%. Logistic Regression, Deep Belief
Network, MLP, Random Forest, and CNN demonstrate
accuracies ranging from 87% to 88%. However, Linear
Discriminant Analysis, PCA, Au-toencoder, and Na ̈ıve
Bayes achieve comparatively lower accuracies, suggesting
potential limitations in capturing the complexity of the
dataset. The proposed audio-based digital testing method
offers a promising approach to COVID-19 detection,
providing a non-invasive and cost-effective solution for
widespread testing and monitoring. The findings highlight
the importance of leveraging machine learning techniques
in healthcare and pave the way for further ad-vancements
in audio-based COVID-19 detection methods.
Keywords :
COVID-19, Cough Diagnosis, Deep Learning, Machine Learning, CNNs, Ensemble Methods, Voting Classifiers, Coughvid Dataset.
The COVID-19 pandemic has caused significant
disruptions to global health, so-ciety, and the economy.
Rapid and accurate detection of COVID-19 is crucial in
minimizing community outbreaks and controlling the
spread of the virus. This study proposes an audio-based
digital testing method for COVID-19, eliminat-ing the need
for patients to travel to testing laboratories. By analyzing
cough noises using machine learning and deep learning
techniques, the presence of COVID-19 can be detected and
classified. The study evaluates multiple ma-chine learning
models on the Coughvid dataset and assesses their
performance in terms of accuracy. The results reveal that
gradient boost achieves the highest accuracy of 88.82%,
followed closely by Xgboost with an accuracy of 88.53%.
Decision tree-based models, such as the Voting Classifier
and Adaboost, also exhibit strong performance with
accuracies above 88%. Logistic Regression, Deep Belief
Network, MLP, Random Forest, and CNN demonstrate
accuracies ranging from 87% to 88%. However, Linear
Discriminant Analysis, PCA, Au-toencoder, and Na ̈ıve
Bayes achieve comparatively lower accuracies, suggesting
potential limitations in capturing the complexity of the
dataset. The proposed audio-based digital testing method
offers a promising approach to COVID-19 detection,
providing a non-invasive and cost-effective solution for
widespread testing and monitoring. The findings highlight
the importance of leveraging machine learning techniques
in healthcare and pave the way for further ad-vancements
in audio-based COVID-19 detection methods.
Keywords :
COVID-19, Cough Diagnosis, Deep Learning, Machine Learning, CNNs, Ensemble Methods, Voting Classifiers, Coughvid Dataset.
