Abstract
The paper describes the process of building a fuzzy expert system for assessing the severity of pneumonia. The use of fuzzy logic is an urgent direction in cases of incomplete certainty when making a medical diagnosis. Human health depends on making the right decision, as it can be difficult for a doctor to choose the correct diagnosis and treatment of pneumonia. To develop a medical expert system, we considered scales and algorithms for assessing the prognosis of the severity of community-acquired pneumonia PORT (PSI), CURB/CRB-65 and SMART-COP/SMART-CO. We strive to contribute the results of our research to the development of medical software products, namely, to increase the efficiency of medical services using artificial intelligence. Python and Prolog programming languages were used to develop the client-server application. The Django framework was used to develop the client part, and the PySwip module was used to process knowledge bases. The knowledge base of the expert system was developed using the SWI-Prolog software environment, which supports the necessary software libraries that provide the construction of the graphical shell of the expert system, as well as dynamic processing of fuzzy rules. The paper outlines the main stages of the life cycle of creating a system using fuzzy logic, which include all the key stages of system design. To test the knowledge base, a graphical interface of the system was developed using the XPCE cross-platform library, which is included in the SWI-Prolog software environment. The purpose of the study is to develop and implement a software module using fuzzy logical inference in a medical information system.
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References
Rudnov, V.A., Fesenko, A.A., Drozd, A.V.: A comparative analysis of diagnostic values of prognostic scales in patients with community acquired pneumonia admitted to ICU. Klinicheskaya mikrobiologiya i antimikrobnaya khimioterapiya 4(9), 330–336 (2007)
Arani, L.A., Sadoughi, F., Langarizadeh, M.: An expert system to diagnose pneumonia using fuzzy logic. Acta Informatica Medica 27(2), 103 (2019)
Nascimento, L.F.C., Rizol, P.M.S.R., Peneluppi, A.P.: Estimating the average length of hospitalization due to pneumonia: a fuzzy approach. Braz. J. Med. Biol. Res. 47, 977–981 (2014)
Wahyuni, E., Ramadhan, A.: Application for the diagnosis of pneumonia based on Pneumonia Severity Index (PSI) values. In: Proceeding of the Electrical Engineering Computer Science and Informatics, vol. 5, no. 1, pp. 107–112 (2018)
Tricahya, S., Rustam, Z.: Forecasting the amount of pneumonia patients in Jakarta with weighted high order fuzzy time series. In: IOP Conference Series: Materials Science and Engineering, vol. 546, no. 5, p. 052080. IOP Publishing (2019)
Chaves, L.E., Nascimento, L.F.C., Rizol, P.M.S.R.: Fuzzy model to estimate the number of hospitalizations for asthma and pneumonia under the effects of air pollution. Rev. Saude Publica 51, 55 (2017)
Pereira, J.C.R., Tonelli, P.A., Barros, L.C., Ortega, N.R.S.: Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory. Braz. J. Med. Biol. Res. 37(5), 701–709 (2004)
Lim, W.S., van der Eerden, M.M., Laing, R., et al.: Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax 58, 377–382 (2003)
Torres, A., Nieto, J.J.: Fuzzy logic in medicine and bioinformatics. J. Biomed. Biotechnol. 2006, 91908 (2006)
Zadeh, L.A.: FuzzySets. Inf. Control 8(3), 338–353 (1965)
Altunin, A.E., Semuhin, M.V.: Models and Algorithms of Decision-Making in Indistinct Conditions: The Monography, p. 352. Publishing House of the Tyumen State University, Tyumen (2000)
Gibadullin, R.F., Zakirov, R.R.: Mobile application for neural network analysis of human functional state. In: Radionov, A.A., Gasiyarov, V.R. (eds.) RusAutoCon 2020. LNEE, vol. 729, pp. 745–755. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-71119-1_73
Gibadullin, R.F., Marushkai, N.S.: Development of predictive CNN based model for vital signs alerts. In: 2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), pp. 404–409 (2021). https://doi.org/10.1109/ICIEAM51226.2021.9446354
Gabdrahmanov, R.T.: Formulation of the task of constructing an expert system for the diagnosis of leukemia. In: Gabdrahmanov, R.T., Hussein, A.H., Burnashev, R.A., Enikeev, A.I. (eds.) Proceedings of the 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4, pp. 7–11 (2019)
Burnashev, R.A.: Expert system building tools based on dynamically updated knowledge. J. Phys.: Conf. Ser. 1352(1), 012008 (2019). R.A. Burnashev, Ismail Amer, A.I. Enikeev
Burnashev, R.A., Enikeev, I.A., Enikeev, A.I.: Design and implementation of integrated development environment for building rule-based expert systems. In: 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), pp. 1–4 (2020). https://doi.org/10.1109/FarEastCon50210.2020.9271143
Forcier, J., Bissex, P., Chun, W.J.: Python Web Development with Django. Addison-Wesley Professional, Boston (2008)
Norris, D.J.: Expert system demonstrations. In: Beginning Artificial Intelligence with the Raspberry Pi, pp. 49–76. Apress, Berkeley, CA (2017)
Mondal, K.C., Nandy, B.D., Baidya, A.: Fact-based expert system for supplier selection with ERP data. In: Mandal, J.K., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds.) Algorithms in Machine Learning Paradigms. SCI, vol. 870, pp. 43–55. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-1041-0_3
Wielemaker, J., Schrijvers, T., Triska, M., Lager, T.: SWI-Prolog. Theory Pract. Logic Program 12(1–2), 67–96 (2012)
Wielemaker, J.: An overview of the SWI-Prolog programming environment. WLPE 3, 1–16 (2003)
Wielemaker, J., Huang, Z., Van Der Meij, L.: SWI-Prolog and the web. Theory Pract. Logic Program. 8(3), 363–392 (2008)
Triska, M.: The boolean constraint solver of SWI-Prolog (system description). In: Kiselyov, O., King, A. (eds.) FLOPS 2016. LNCS, vol. 9613, pp. 45–61. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29604-3_4
Alaiba, V., Rotaru, A.: Agent architecture for building Robocode players with SWI-Prolog. In: 2008 International Multiconference on Computer Science and Information Technology, pp. 3–7 (2008). https://doi.org/10.1109/IMCSIT.2008.4747210
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This paper has been supported by the Kazan Federal University Strategic Academic Leadership Program (PRIORITY-2030).
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Burnashev, R., Enikeeva, A., Amer, I.F., Akhmedova, A., Bolsunovskaya, M., Enikeev, A. (2023). Building a Fuzzy Expert System for Assessing the Severity of Pneumonia. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-031-16075-2_27
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