Express Diagnosis of COVID-19 on Cough Audiograms with Machine Learning Algorithms from Scikit-learn library and GMDH Shell tool

The ongoing COVID-19 pandemic and necessity of mass control of population makes to create inexpensive rapid diagnostic methods that could replace or complement existing methods based on clinical studies. In response to this challenge, at the end of 2020 MIT scientists proposed a way to detect COVID-19 sick patients using audio recordings of their cough. They build a binary classifier based on a trained deep neural network that provides 99% precision in detecting sick patients on a dataset of 5000 people (the precision of detecting the healthy ones is not reported). In our study, we propose another technology, which uses: (a) a simple transformation of digital audiograms being matrices ‘fequency-time'and (b) typical machine learning algorithms from the popular scikit-learn Python library and the platform GMDH Shell. Objects of consideration are: a large unbalanced dataset (282 sick and 1595 healthy) and a small balanced dataset (174 sick and 193 healthy). In total GMDH-based algorithms demonstrated some better results with both datasets. The winners provides the following precisions of detecting sick/healthy patients [%]: (a) 92/95 on the small dataset and 78/95 on the large data set for the algorithm SVM with a Gaussian kernel; (b) 95/97 on the small dataset and 82/96 on the large data set for the algorithm Random Forest based on GMDH. We suppose these results are promising.