# An Ensemble-based Multi-Criteria Decision Making Method for COVID-19 Cough Classification

arXiv: https://arxiv.org/abs/2110.00508 · HTML: https://mlsanity.com/paper/2110.00508/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 54 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data](https://mlsanity.com/paper/2006.05919/) (2020)
- [The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms](https://mlsanity.com/paper/2009.11644/) (2020)
- [Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis](https://mlsanity.com/paper/2005.10548/) (2020)
- [AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App](https://mlsanity.com/paper/2004.01275/) (2020)
- [COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings](https://mlsanity.com/paper/2012.01926/) (2020)
- [Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds](https://mlsanity.com/paper/2009.08790/) (2020)
- [Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough](https://mlsanity.com/paper/2011.13320/) (2020)
- [Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing](https://mlsanity.com/paper/2010.02417/) (2020)
- [Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks](https://mlsanity.com/paper/2012.14553/) (2020)
- [Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning](https://mlsanity.com/paper/2004.08287/) (2020)
- [Can Machine Learning Be Used to Recognize and Diagnose Coughs?](https://mlsanity.com/paper/2004.01495/) (2020)
- [PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images](https://mlsanity.com/paper/2007.14777/) (2020)
- [ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays](https://mlsanity.com/paper/2009.11850/) (2020)
- [Virufy: A Multi-Branch Deep Learning Network for Automated Detection of COVID-19](https://mlsanity.com/paper/2103.01806/) (2021)
- [Diagnosis of COVID-19 and Non-COVID-19 Patients by Classifying Only a Single Cough Sound](https://mlsanity.com/paper/2102.04880/) (2021)
- [QUCoughScope: An Artificially Intelligent Mobile Application to Detect Asymptomatic COVID-19 Patients using Cough and Breathing Sounds](https://mlsanity.com/paper/2103.12063/) (2021)

## Abstract (arXiv)

The objectives of this research are analysing the performance of the state-of-the-art machine learning techniques for classifying COVID-19 from cough sound and identifying the model(s) that consistently perform well across different cough datasets. Different performance evaluation metrics (such as precision, sensitivity, specificity, AUC, accuracy, etc.) make it difficult to select the best performance model. To address this issue, in this paper, we propose an ensemble-based multi-criteria decision making (MCDM) method for selecting top performance machine learning technique(s) for COVID-19 cough classification. We use four cough datasets, namely Cambridge, Coswara, Virufy, and NoCoCoDa to verify the proposed method. At first, our proposed method uses the audio features of cough samples and then applies machine learning (ML) techniques to classify them as COVID-19 or non-COVID-19. Then, we consider a multi-criteria decision-making (MCDM) method that combines ensemble technologies (i.e., soft and hard) to select the best model. In MCDM, we use the technique for order preference by similarity to ideal solution (TOPSIS) for ranking purposes, while entropy is applied to calculate evaluation criteria weights. In addition, we apply the feature reduction process through recursive feature elimination with cross-validation under different estimators. The results of our empirical evaluations show that the proposed method outperforms the state-of-the-art models.