Moreover, the performed analysis resulted in an online service ([[https://decode.polsl.pl](https://decode.polsl.pl)]([https://decode.polsl.pl](https://decode.polsl.pl)), accessed on 25 October 2021) available for anyone who needed support in COVID-19 screening in the early stages of the pandemic. Due to the mutations of the SARS-CoV-2 virus, the developed screening models, which are presented in this paper and implemented on the online service, are no longer valid. However, if there was such a need, new models could be generated according to the procedure presented in the following sections. The contribution of this work is manifold and it consists of the following elements: \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{3}{c}{**Valuation by**} & \multicolumn{2}{c}{**Valuation by**} & \multicolumn{2}{c}{**Daily Number**} \\  & \multicolumn{2}{c}{**Laboratories (USD)**} & \multicolumn{2}{c}{**the Ministry (USD)**} & \multicolumn{2}{c}{**of Tests Performed**} \\ \hline  & **Min** & **Max** & **Mean** & & **Min** & **Max** & **Mean** \\ \hline April 2020 & 96 & 120 & 108 & 108 & 4382 & 14,482 & 9554 \\ July 2020 & 100 & 137 & 103 & 63 & 15,464 & 35,127 & 23,178 \\ June 2021 & 72 & 135 & 107 & 75 & 27,606 & 64,943 & 50,238 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Costs of the RT-PCR test evaluated by analytical laboratories and the Ministry of Health in Poland. the presentation of the architecture and workflow of the screening support system, which, at a time when little is known about the disease, can, with a high degree of certainty, screen out patients and identify those who most probably do not have the disease, and redirect the rest for further costly diagnostic tests;
* the case study presenting the method for the generation of classifiers, where the models are tuned to an assumed acceptable threshold of negative predictive values so that the results allow for the effective screening of patients for COVID-19;
* an explanation of the obtained classification models, enabling the users to understand the basis of the decision made by the model;
* a new data set based on questionnaires collected in a hospital in Poland and the data set preparation, processing, characterisation, and visualisation, leading to the identification of COVID-19 characteristics. The structure of this paper is as follows. Section 2 presents an overview of previous research related to the topic of this work. The proposed workflow and system architecture are presented in Section 3. Section 4 outlines the characteristics of the shared data set being the basis for classification models. Section 5 presents an automatic machine learning approach and advanced approach to produce a classifier. The latter consists of advanced data preparation steps and the generation and tuning of two models. Section 6 focuses on the evaluation of the created models. Section 7 is devoted to the issue of model explanation, and, additionally, it addresses the issue of model adaptation and the cost reduction that can be realised by the presented system. Section 8 outlines the developed web application, which is able to make an initial diagnosis (screening) based on the given symptoms. Section 9 concludes the paper.