  The epidemic disease, called the new coronavirus (COVID19), firstly occurred
in Wuhan, China in December 2019. COVID19 was announced as an epidemic by World
Health 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 in
fighting the pandemic and controlling the epidemic is the early diagnosis of
COVID19(+) patients and the follow-up of these patients. Therefore, various
diagnostic mechanisms are used. Additionally to the RT-PCR test, medical
imaging methods have been utilized, especially in the detection of COVID19(+)
patients. In this study, an alternative approach was proposed by using cough
data, which is one of the most prominent symptoms of COVID19(+) patients. The
cough acoustic public dataset on the Virufy website was used. The entire data
was normalized using z-normalization technique. The performance of the features
obtained via the 5-layer empirical mode decomposition method and the
performances of different classifiers has been compared. As the classifier
algorithm, 5 different algorithms were used. The highest accuracy and F1-score
performances were obtained by using Ensemble-Bagged-Trees algorithm as 90.6%
and 90.5%, respectively. On the other hand, other classification algorithms
used in the study are Support Vector Machines, Logistic Regression, Linear
Discriminant Analysis and k-Nearest Neigbors, respectively. According to the
results obtained, choosing the right classifier algorithm provides high
results. Thus, it is clear that using cough acoustic data, those with
COVID19(+) can be detected easily and effectively.
