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Related Experiment Video

Updated: Oct 31, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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COVID-19 cough classification using machine learning and global smartphone recordings.

Madhurananda Pahar1, Marisa Klopper2, Robin Warren2

  • 1Department of Electrical and Electronic Engineering, Stellenbosch University, South Africa.

Computers in Biology and Medicine
|June 28, 2021
PubMed
Summary

Machine learning accurately identifies COVID-19 (Coronavirus Disease 2019) from cough sounds using smartphone recordings. This non-contact screening method aids early detection and reduces transmission risks.

Keywords:
COVID-19Convolutional neural network (CNN)Cough classificationK-nearest neighbour (KNN)Logistic regression (LR)Long short-term memory (LSTM)Machine learningMultilayer perceptron (MLP)Resnet50Support vector machine (SVM)

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Area of Science:

  • Artificial Intelligence
  • Biomedical Engineering
  • Public Health

Background:

  • COVID-19 (Coronavirus Disease 2019) screening is crucial for limiting transmission.
  • Current testing methods can be resource-intensive and pose risks of exposure.
  • A non-contact, accessible screening tool is needed to supplement existing diagnostics.

Purpose of the Study:

  • To develop and evaluate a machine learning model for classifying COVID-19 positive coughs.
  • To differentiate COVID-19 coughs from healthy and other non-COVID-19 coughs.
  • To assess the feasibility of smartphone-based cough audio analysis for public health screening.

Main Methods:

  • Utilized two datasets including global and South African subjects with forced and natural coughs.
  • Applied the synthetic minority oversampling technique (SMOTE) to address dataset imbalance.
  • Trained and evaluated seven machine learning classifiers, including Resnet50 and LSTM, using a leave-p-out cross-validation scheme.

Main Results:

  • Resnet50 achieved an Area Under the ROC Curve (AUC) of 0.98 for distinguishing COVID-19 positive from healthy coughs.
  • An LSTM classifier achieved an AUC of 0.94 for differentiating COVID-19 positive from COVID-19 negative coughs.
  • COVID-19 positive coughs were found to be 15%-20% shorter than non-COVID coughs across datasets.

Conclusions:

  • Machine learning-based cough classification is a viable and cost-effective method for non-contact COVID-19 screening.
  • Smartphone-based analysis offers a scalable solution for early detection and self-isolation recommendations.
  • This approach can potentially reduce the burden on testing centers and mitigate virus spread.