  In this study, a machine learning model was developed for automatically
detecting respiratory system sounds such as sneezing and coughing in disease
diagnosis. The automatic model and approach development of breath sounds, which
carry valuable information, results in early diagnosis and treatment. A
successful machine learning model was developed in this study, which was a
strong response to the challenge called the "Pfizer digital medicine challenge"
on the "OSFHOME" open access platform. "Environmental sound classification"
called ESC-50 and AudioSet sound files were used to prepare the dataset. In
this dataset, which consisted of three parts, features that effectively showed
coughing and sneezing sound analysis were extracted from training, testing and
validating samples. Based on the Mel frequency cepstral coefficients (MFCC)
feature extraction method, mathematical and statistical features were prepared.
Three different classification techniques were considered to perform successful
respiratory sound classification in the dataset containing more than 3800
different sounds. Support vector machine (SVM) with radial basis function (RBF)
kernels, ensemble aggregation and decision tree classification methods were
used as classification techniques. In an attempt to classify coughing and
sneezing sounds from other sounds, SVM with RBF kernels was achieved with 83%
success.
