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The objectives of this research are analysing the performance of thestate-of-the-art machine learning techniques for classifying COVID-19 fromcough sound and identifying the model(s) that consistently perform well acrossdifferent cough datasets. Different performance evaluation metrics (such asprecision, sensitivity, specificity, AUC, accuracy, etc.) make it difficult toselect the best performance model. To address this issue, in this paper, wepropose an ensemble-based multi-criteria decision making (MCDM) method forselecting top performance machine learning technique(s) for COVID-19 coughclassification. We use four cough datasets, namely Cambridge, Coswara, Virufy,and NoCoCoDa to verify the proposed method. At first, our proposed method usesthe 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 amulti-criteria decision-making (MCDM) method that combines ensembletechnologies (i.e., soft and hard) to select the best model. In MCDM, we usethe technique for order preference by similarity to ideal solution (TOPSIS) forranking purposes, while entropy is applied to calculate evaluation criteriaweights. In addition, we apply the feature reduction process through recursivefeature elimination with cross-validation under different estimators. Theresults of our empirical evaluations show that the proposed method outperformsthe state-of-the-art models.

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