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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method.

Nihad Karim Chowdhury¹, Muhammad Ashad Kabir², Md Muhtadir Rahman¹

¹Department of Computer Science and Engineering, University of Chittagong, Bangladesh.

Computers in Biology and Medicine

|March 23, 2022

Summary

This study introduces a novel ensemble-based multi-criteria decision-making (MCDM) method to effectively select top-performing machine learning models for COVID-19 cough classification. The proposed approach demonstrates superior performance over existing methods in identifying COVID-19 from cough sounds.

Keywords:

COVID-19 Classification Cough Ensemble Entropy MCDM Machine learning TOPSIS

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Area of Science:

Artificial Intelligence
Medical Informatics
Signal Processing

Background:

Classifying COVID-19 using cough sounds presents challenges due to varied performance metrics.
Selecting the optimal machine learning model for COVID-19 cough classification is complex.
Existing methods struggle to consistently identify high-performing models across diverse datasets.

Purpose of the Study:

To propose and validate an ensemble-based multi-criteria decision-making (MCDM) method for selecting superior machine learning techniques for COVID-19 cough classification.
To evaluate the performance of state-of-the-art machine learning models using multiple cough datasets.
To address the difficulty in selecting the best model due to diverse performance evaluation metrics.

Main Methods:

Utilized audio features from four distinct cough datasets (Cambridge, Coswara, Virufy, NoCoCoDa).
Applied machine learning (ML) techniques for binary classification of COVID-19 versus non-COVID-19 coughs.
Implemented an ensemble-based MCDM approach combining soft and hard voting, TOPSIS for ranking, entropy for weight calculation, and recursive feature elimination for feature reduction.

Main Results:

The proposed ensemble-based MCDM method demonstrated superior performance compared to state-of-the-art models.
Empirical evaluations confirmed the effectiveness of the method across multiple datasets.
Using the Extra-Trees classifier within the proposed framework yielded excellent results, achieving an AUC of 0.95, Precision of 1, and Recall of 0.97.

Conclusions:

The developed ensemble-based MCDM method offers a robust solution for selecting optimal machine learning models for COVID-19 cough classification.
This approach effectively handles the complexity arising from multiple performance metrics and diverse datasets.
The findings highlight the potential of AI-driven cough analysis for efficient COVID-19 detection.