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Abstract

Efforts of the scientific community led to the development of multiple screening approaches for COVID-19 that rely on machine learning methods. However, there is a lack of works showing how to tune the classification models used for such a task and what the tuning effect is in terms of various classification quality measures. Understanding the impact of classifier tuning on the results obtained will allow the users to apply the provided tools consciously. Therefore, using a given screening test they will be able to choose the threshold value characterising the classifier that gives, for example, an acceptable balance between sensitivity and specificity. The presented work introduces the optimisation approach and the resulting classifiers obtained for various quality threshold assumptions. As a result of the research, an online service was created that makes the obtained models available and enables the verification of various solutions for different threshold values on new data.
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Authors and Affiliations

Michał Kozielski
1
ORCID: ORCID
Joanna Henzel
1
ORCID: ORCID
Joanna Tobiasz
2
ORCID: ORCID
Aleksandra Gruca
1
Paweł Foszner
3
ORCID: ORCID
Joanna Zyla
2
ORCID: ORCID
Małgorzata Bach
4
Aleksandra Werner
4
ORCID: ORCID
Jerzy Jaroszewicz
5
Joanna Polańska
2
ORCID: ORCID
Marek Sikora
1
ORCID: ORCID

  1. Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
  2. Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
  3. Department of Graphics, Computer Vision and Digital Systems, Silesian University of Technology, Gliwice, Poland
  4. Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
  5. Department of Infectious Diseases and Hepatology, Medical University of Silesia, Katowice, Poland

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