  Distinguishing COVID-19 from other flu-like illnesses can be difficult due to
ambiguous symptoms and still an initial experience of doctors. Whereas, it is
crucial to filter out those sick patients who do not need to be tested for
SARS-CoV-2 infection, especially in the event of the overwhelming increase in
disease. As a part of the presented research, logistic regression and XGBoost
classifiers, that allow for effective screening of patients for COVID-19, were
generated. Each of the methods was tuned to achieve an assumed acceptable
threshold of negative predictive values during classification. Additionally, an
explanation of the obtained classification models was presented. The
explanation enables the users to understand what was the basis of the decision
made by the model. The obtained classification models provided the basis for
the DECODE service (decode.polsl.pl), which can serve as support in screening
patients with COVID-19 disease. Moreover, the data set constituting the basis
for the analyses performed is made available to the research community. This
data set consisting of more than 3,000 examples is based on questionnaires
collected at a hospital in Poland.
