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

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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.
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art. no. e137349
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
  • Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
  • Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
  • Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
  • Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
  • Department of Graphics, Computer Vision and Digital Systems, Silesian University of Technology, Gliwice, Poland
autor
  • Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
  • Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
  • Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
  • Department of Infectious Diseases and Hepatology, Medical University of Silesia, Katowice, Poland
  • Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
autor
  • Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
Bibliografia
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  • [8] R. Trevethan, “Sensitivity, specificity, and predictive values: Foundations, pliabilities, and pitfalls in research and practice”, Front. Public Health, vol. 5, p. 307, 2017.
  • [9] J. Henzel et al., “Classification supporting COVID-19 diagnostics based on patient survey data”, arXiv:2011.12247, 2020.
  • [10] H. Swapnarekha, H.S. Behera, J. Nayak, and B. Naik, “Role of intelligent computing in COVID-19 prognosis: A state-of-theart review”, Chaos Solitons Fractals, vol. 138, p. 109947, 2020.
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  • [14] T. Ozturk, M. Talo, E.A. Yildirim, U.B. Baloglu, O. Yildirim, and U. Rajendra Acharya, “Automated detection of COVID-19 cases using deep neural networks with x-ray images”, Comput. Biol. Med., vol. 121, p. 103792, 2020.
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  • [18] J. Laguarta, F. Hueto, and B. Subirana, “COVID-19 artificial intelligence diagnosis using only cough recordings”, IEEE Eng. Med. Biol. Mag., vol. 1, pp. 275‒281, 2020.
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  • [20] C. Feng et al., “A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics”, medRxiv 2020.03.19.20039099.
  • [21] D. Brinati, A. Campagner, D. Ferrari, M. Locatelli, G. Banfi, and F. Cabitza, “Detection of COVID-19 infection from routine blood exams with machine learning: a feasibility study”, J. Med. Syst., vol. 44, p. 135, 2020.
  • [22] “Suspected COVID-19 pneumonia diagnosis aid system”, [Online]. Available: https://intensivecare.shinyapps.io/COVID19/, (Accessed on 28/12/2020).
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  • [24] Symptomate, “Symptomate COVID-19 risk assessment tool”, [Online]. Available: https://symptomate.com/covid19/checkup, (Accessed on 12/28/2020).
  • [25] CDC, “Testing for COVID-19”, [Online]. Available: https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/testing.html, (Accessed on 28/12/2020).
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  • [27] “COVID-19 Risk Assessment”, [Online]. Available: https://covid.preflet.com/en, (Accessed on 28/12/2020).
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  • [29] P. McCullagh, J.A. Nelder, Generalized Linear Models, 2nd ed. Chapman & Hall, 1989.
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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).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cdb77bad-20a2-4f5d-bda5-9c3e321a8c60
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