Enhancement of COVID-19 symptom-based screening with quality-based classifier optimisation

• Autorzy: Kozielski Michał , Henzel Joanna , Tobiasz Joanna , Gruca Aleksandra , Foszner Paweł , Zyla Joanna , Bach Małgorzata , Werner Aleksandra , Jaroszewicz Jerzy , Polańska Joanna , Sikora Marek

Identyfikatory

DOI

10.24425/bpasts.2021.137349

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

classification; classification quality; screening; COVID-19

PL

klasyfikacja; jakość klasyfikacji; badania przesiewowe; COVID-19

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2021
• Tom: Vol. 69, nr 4
• Strony: art. no. e137349
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Kozielski Michał

• Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland

• https://orcid.org/0000-0003-3573-7638

autor

Henzel Joanna

• Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland

• https://orcid.org/0000-0001-7821-1375

autor

Tobiasz Joanna

• Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland

• https://orcid.org/0000-0002-4666-1336

autor

Gruca Aleksandra

• Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland

autor

Foszner Paweł

• Department of Graphics, Computer Vision and Digital Systems, Silesian University of Technology, Gliwice, Poland

• https://orcid.org/0000-0001-5491-9096

autor

Zyla Joanna

• Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland

• https://orcid.org/0000-0002-2895-7969

autor

Bach Małgorzata

• Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland

autor

Werner Aleksandra

• Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland

• https://orcid.org/0000-0001-6098-0088

autor

Jaroszewicz Jerzy

• Department of Infectious Diseases and Hepatology, Medical University of Silesia, Katowice, Poland

autor

Polańska Joanna

• Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland

• https://orcid.org/0000-0001-8004-9864

autor

Sikora Marek

• Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland

• https://orcid.org/0000-0002-2393-9761

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).