  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.
