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In this study, we proposed a machine learning-based system to distinguishpatients with COVID-19 from non-COVID-19 patients by analyzing only a singlecough sound. Two different data sets were used, one accessible for the publicand the other available on request. After combining the data sets, the featureswere obtained from the cough sounds using the mel-frequency cepstralcoefficients (MFCCs) method, and then they were classified with seven differentmachine learning classifiers. To determine the optimum values ofhyperparameters for MFCCs and classifiers, the leave-one-out cross-validation(LOO-CV) strategy was implemented. Based on the results, the k-nearestneighbors classifier based on the Euclidean distance (k-NN Euclidean) with theaccuracy rate, sensitivity of COVID-19, sensitivity of non-COVID-19, F-measure,and area under the ROC curve (AUC) of 0.9833, 1.0000, 0.9720, 0.9799, and0.9860, respectively, is more successful than other classifiers. Finally, thebest and most effective features were determined for each classifier using thesequential forward selection (SFS) method. According to the results, theproposed system is excellent compared with similar studies in the literatureand can be easily used in smartphones and facilitate the diagnosis of COVID-19patients. In addition, since the used data set includes reflex and unconsciouscoughs, the results showed that conscious or unconscious coughing has no effecton the diagnosis of COVID-19 patients based on the cough sound.

Diagnosis of COVID-19 and Non-COVID-19 Patients by Classifying Only a Single Cough Sound