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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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Tom
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art. no. e137349
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
autor
- Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
autor
- Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
autor
- 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
autor
- 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
autor
- Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
autor
- Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
autor
- Department of Infectious Diseases and Hepatology, Medical University of Silesia, Katowice, Poland
autor
- 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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- [6] L. Wynants et al., “Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal”, BMJ, vol. 369, p. m1328, 2020.
- [7] A. Callahan et al., “Estimating the efficacy of symptom-based screening for COVID-19”, NPJ Digit. Med., vol. 3, no. 1, pp. 1–3, 2020.
- [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.
- [11] S. Lalmuanawma, J. Hussain, and L. Chhakchhuak, “Applications of machine learning and artificial intelligence for COVID-19 (SARS-CoV-2) pandemic: A review”, Chaos Solitons Fractals, vol. 139, p. 110059, 2020.
- [12] Y. Mohamadou, A. Halidou, and P.T. Kapen, “A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19”, Appl. Intell., vol. 50, no. 11, pp. 3913–3925, 2020.
- [13] A. Banerjee et al., “Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population”, Int. Immunopharmacol., vol. 86, p. 106705, 2020.
- [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.
- [15] R.M. Pereira, D. Bertolini, L.O. Teixeira, C.N. Silla, and Y.M. Costa, “COVID-19 identification in chest x-ray images on flat and hierarchical classification scenarios”, Comput. Meth. Programs Biomed., vol. 194, p. 105532, 2020.
- [16] D. Singh, V. Kumar, V. Yadav, and M. Kaur, “Deep neural network-based screening model for COVID-19-infected patients using chest x-ray images”, Int. J. Pattern Recogn. Artif. Intell., vol. 25, no. 3, p. 2151004, 2021.
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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.
- [19] P. Bagad et al., “Cough against covid: Evidence of covid-19 signature in cough sounds”, arXiv:2009.08790 (2020).
- [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).
- [23] “ML-based COVID-19 test from routine blood test”, [Online]. Available: https://covid19-blood-ml.herokuapp.com/, (Accessed on 28/12/2020).
- [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).
- [26] Apple Inc., “Apple covid-19”, [Online]. Available: https://covid19.apple.com/screening, (Accessed on 28/12/2020).
- [27] “COVID-19 Risk Assessment”, [Online]. Available: https://covid.preflet.com/en, (Accessed on 28/12/2020).
- [28] M. DataLab, M. Groups, “COVID-19 risk calculator”. [Online]. Available: https://crs19.pl/ (Accessed on 28/12/2020).
- [29] P. McCullagh, J.A. Nelder, Generalized Linear Models, 2nd ed. Chapman & Hall, 1989.
- [30] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System”, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
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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