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The epidemic disease, called the new coronavirus (COVID19), firstly occurredin Wuhan, China in December 2019. COVID19 was announced as an epidemic by WorldHealth Organization soon after. Some of the symptoms of this disease are fever,cough, shortness of breath and difficulty in breathing. In more severe cases,death may occur as a result of infection. The most significant question infighting the pandemic and controlling the epidemic is the early diagnosis ofCOVID19(+) patients and the follow-up of these patients. Therefore, variousdiagnostic mechanisms are used. Additionally to the RT-PCR test, medicalimaging methods have been utilized, especially in the detection of COVID19(+)patients. In this study, an alternative approach was proposed by using coughdata, which is one of the most prominent symptoms of COVID19(+) patients. Thecough acoustic public dataset on the Virufy website was used. The entire datawas normalized using z-normalization technique. The performance of the featuresobtained via the 5-layer empirical mode decomposition method and theperformances of different classifiers has been compared. As the classifieralgorithm, 5 different algorithms were used. The highest accuracy and F1-scoreperformances were obtained by using Ensemble-Bagged-Trees algorithm as 90.6%and 90.5%, respectively. On the other hand, other classification algorithmsused in the study are Support Vector Machines, Logistic Regression, LinearDiscriminant Analysis and k-Nearest Neigbors, respectively. According to theresults obtained, choosing the right classifier algorithm provides highresults. Thus, it is clear that using cough acoustic data, those withCOVID19(+) can be detected easily and effectively.

Comparison of Classification Algorithms for COVID19 Detection using Cough Acoustic Signals