  Audio classification using breath and cough samples has recently emerged as a
low-cost, non-invasive, and accessible COVID-19 screening method. However, a
comprehensive survey shows that no application has been approved for official
use at the time of writing, due to the stringent reliability and accuracy
requirements of the critical healthcare setting. To support the development of
Machine Learning classification models, we performed an extensive comparative
investigation and ranking of 15 audio features, including less well-known ones.
The results were verified on two independent COVID-19 sound datasets. By using
the identified top-performing features, we have increased COVID-19
classification accuracy by up to 17% on the Cambridge dataset and up to 10% on
the Coswara dataset compared to the original baseline accuracies without our
feature ranking.
