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

• URL: http://arxiv.org/abs/2110.00508v1
• Date: Fri, 1 Oct 2021 16:19:50 GMT
• Title: An Ensemble-based Multi-Criteria Decision Making Method for COVID-19 Cough Classification
• Authors: Nihad Karim Chowdhury, Muhammad Ashad Kabir, Md. Muhtadir Rahman
• Abstract summary: 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. The results of our empirical evaluations show that the proposed method outperforms the state-of-the-art models.
• Score: 2.1915057426589746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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