@ARTICLE{Kozielski_Michał_Enhancement_2021,
 author={Kozielski, Michał and Henzel, Joanna and Tobiasz, Joanna and Gruca, Aleksandra and Foszner, Paweł and Zyla, Joanna and Bach, Małgorzata and Werner, Aleksandra and Jaroszewicz, Jerzy and Polańska, Joanna and Sikora, Marek},
 volume={69},
 number={4},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e137349},
 howpublished={online},
 year={2021},
 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.},
 type={Article},
 title={Enhancement of COVID-19 symptom-based screening with quality-based classifier optimisation},
 URL={http://ochroma.man.poznan.pl/Content/119743/PDF/07_02102_Bpast.No.69(4)_27.08.21_druk.pdf},
 doi={10.24425/bpasts.2021.137349},
 keywords={classification, classification quality, screening, COVID-19},
}