- Tytuł:
- 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 - Powiązania:
- https://bibliotekanauki.pl/articles/2086854.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
classification
classification quality
screening
COVID-19
klasyfikacja
jakość klasyfikacji
badania przesiewowe - Opis:
- 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.
- Źródło:
-
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 4; e137349, 1--8
0239-7528 - Pojawia się w:
- Bulletin of the Polish Academy of Sciences. Technical Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł