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Speech-based respiratory diagnostics: A study on COVID-19 detection with machine learning.

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  • 1Department of Computer Science Engineering, Ramrao Adik Institute of Technology, Navi Mumbai, India.

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Summary
This summary is machine-generated.

This study shows Random Forest and ANOVA can accurately detect COVID-19 using vowel sounds. This non-invasive method aids remote respiratory disease diagnosis.

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Area of Science:

  • Medical Informatics
  • Signal Processing
  • Machine Learning

Background:

  • Respiratory sound analysis offers a non-invasive method for diagnosing respiratory conditions like COVID-19.
  • Vowel phonations (/a/, /e/, /o/) contain acoustic markers relevant to respiratory health.
  • The COSWARA dataset provides speech samples for respiratory disease research.

Purpose of the Study:

  • To evaluate the efficacy of OpenSMILE audio features for COVID-19 detection using specific vowel sounds.
  • To compare the performance of various machine learning classifiers and feature selection techniques for this task.
  • To identify the optimal combination of features and classifiers for accurate COVID-19 detection.

Main Methods:

  • Extraction of audio and functional features using OpenSMILE from vowel sounds /a/, /e/, /o/.
  • Classification using Random Forest (RF), Support Vector Machine, Decision Tree, and Artificial Neural Network models.
  • Application of five feature selection methods (ANOVA, chi-square, Information Gain, ReliefF, Gini index) to enhance classification.
  • Statistical validation using the Friedman test to assess model and feature selection performance.

Main Results:

  • ANOVA-based feature selection demonstrated consistent performance across classifiers and vowel sounds.
  • The Random Forest classifier, combined with ANOVA-selected features, achieved the highest accuracies (76.47% for /a/, 75.54% for /a/+/o/).
  • The Friedman test confirmed the robustness of the Random Forest and ANOVA combination.

Conclusions:

  • Random Forest with ANOVA-selected features is a significant and robust approach for COVID-19 detection via vowel sound analysis.
  • This method contributes to developing accessible, scalable, and non-invasive diagnostic tools for respiratory diseases.
  • The findings support the integration of such technologies into telemedicine for early disease detection and remote healthcare.