Development of an intelligent diagnostic model for pulmonological diseases based on fuzzy logic

Authors and copyright holders:

• Dubinin N. M. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
• Gandzha T. V. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
• Kochergin M. I. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
• Filinyuk O. V. , Siberian State Medical University (Tomsk, Russia)
• Dmitriev V. M. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)

• 1. Stivaros S.M., Gledson G., Nenadic G., Zeng X.-J., Keane J., Jackson A. Decision support systems for clinical radiological practice – towards the next generation. British Journal of Radiology, 2010, vol. 83, no. 995, pp. 904–914.
• 2. Berner E.S. (ed.). Clinical decision support systems. New York, Springer Publ., 2007, 270 p.
• 3. Khosla Vinod. Technology will replace 80% of what doctors do. Available at: https://web.archive.org/web/20130328173118/http://tech.fortune.cnn.com/2012/12/04/technologydoctors-khosla / (Аccessed: 1 February 2025).
• 4. Dehghani Soufi M., Samad-Soltani T., Shams Vahdati S., Rezaei-Hachesu P. Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic. International Journal of Medical Informatics, 2018, vol. 114, pp. 35–44.
• 5. Tanveer Syeda-Mahmood. The Role of Machine Learning in Clinical Decision Support. SPIE Newsroom, 2015. Available at: URL: https://www.spie.org/news/mi-plenary_landing/mi-plenary_syeda-mahmood (Аccessed: 1 February 2025).
• 6. Wagholikar K, Sundararajan V., Deshpande A. Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Direction. Journal of Medical Systems, 2012, vol. 36, no. 5, pp. 3029–3049.
• 7. Demushkina K.M., Demushkin M.O., Kuzmin A.V. [Review of methods for designing medical decision support systems]. Models, systems, networks in economics, technology, nature and society, 2022, no. 42, pp. 75–89 (in Russ.).
• 8. Grishunov S.S., Kozina A.V., Nosova Yu.S. [On the issue of applying machine learning methods to solve the problem of diagnosing heart diseases]. Electronic journal: Science, Technology and Education, 2018, no. SV2/2018 (20), pp. 37–46. Available at: URL: http://nto-journal.ru/uploads/articles/2b705aada13b2f9b547ab0bf72ef1744.pdf (Аccessed: 1 February 2025).
• 9. Pavlova A.I. [Application of machine learning algorithms for predicting heart diseases]. Siberian Journal of Natural Sciences and Agriculture, 2023, vol. 15, no. 3, pp. 475–496 (in Russ.).
• 10. Utkin L.V., Mehldau A.A., Kovalev M.S. and Kasimov E.M. [Review of methods of explanation and interpretation of decision-making in intelligent systems for the diagnosis of oncological diseases]. Artificial intelligence and decisionmaking, 2020, no. 3, pp. 55–65 (in Russ.).
• 11. Runova K.V., Yurin A.A. [Classification of cardiovascular diseases using instrumental methods of information processing based on various machine learning methods]. Bulletin of Kazan Technological University, 2019, vol. 22, no. 3, pp. 45–53 (in Russ.).
• 12. Kozlov V.V, Kozlov A.V. [Decision support system for the diagnosis of lung diseases]. The world of science without borders, 2011, vol. 18, no. 1, pp. 187–188 (in Russ.).
• 13. Kostin K.A. Lamonova T.S. Klassifikaciya patologij disseminirovannogo tuberkulyoza lyogkikh s pomoshch'yu metodov mashinnogo obucheniya [Classification of pathologies of disseminated pulmonary tuberculosis using machine learning methods]. Collection of selected articles of the TUSUR Scientific Session, Tomsk, May 16–18, 2018, in 3 parts, Tomsk, V-Spectr, 2018, part 3, pp. 129–132 (in Russ.).
• 14. Lapteva E.A., Kovalenko I.V., Laptev A.N. et al. [The use of «neural network» technology for the detection and monitoring of auscultation phenomena in the diagnosis of respiratory diseases]. Journal of the Grodno State Medical University. 2020, vol. 18, no. 3, pp. 230–235 (in Russ.).
• 15. Yusupova N.I., Bogdanov M.R., Smetanina O.N. [Reducing risks when using machine learning in the diagnosis of bronchopulmonary diseases]. Artificial intelligence and decision-making, 2023, no. 1, pp. 42–54 (in Russ.).
• 16. Boyarintsev L.A. Trofimenko A.V., Stebeltsov S.V. et al. Razrabotka sistemy rannej diagnostiki vnebol'nichnoj pnevmonii, covid-19, tuberkuleza i drugikh zabolevanij legkikh s ispol'zovaniem iskusstvennogo intellekta [Development of a system for early diagnosis of community-acquired pneumonia, COVID-19, tuberculosis and other lung diseases using artificial intelligence]. Proceedings of II All-Russian scientific and technical conference «State and prospects of development of modern science in the field of «biotechnical systems and technologies», Anapa, FGAU VIT «ERA», 2021, pp. 45–49 (in Russ.).
• 17. Yusupova, N.I., Nurgayanova O.S., Zulkarneev R.H. [Formalization of the stages of risk analysis in SPD taking into account clinical risk assessments in bronchopulmonary diseases]. System engineering and information technologies, 2023, vol. 5, no. 1(10), pp. 11–24. DOI: 10.54708/2658-5014-SIIT2023-no1-p11 (in Russ.).
• 18. Yusupova N.I., Bogdanov M.R., Smetanina O.N. [Risk reduction using machine learning in the diagnosis of bronchopulmonary diseases]. Artificial intelligence and decision-making, 2023, no. 1, pp. 42–54. DOI 10.14357/20718594230105 (in Russ.).
• 19. Khodashinsky I.A., Smirnova I.N., Bardamova M.B., Sarin K.S. et al. [A method for finding subsets of consistent signs in predicting the effectiveness of rehabilitation of patients after coronavirus infection]. Siberian Journal of Clinical and Experimental Medicine, 2023, vol. 38, no. 4, pp. 270–279. (in Russ.).
• 20. Gusev A.V. Novitsky R.E., Ivshin A.A., Alekseev A.A. [Machine learning on laboratory data for disease prediction]. Pharmacoeconomics. Modern pharmacoeconomics and pharmacoepidemiology, 2021, vol. 14, no. 4, pp. 581–592 (in Russ.).
• 21. Mazitova N.N., Berheeva Z.M. [Application of a systematic approach in the diagnosis of occupational diseases of respiratory organs]. Public health and healthcare, 2013, vol. 53, no. 3, pp. 20–25 (in Russ.).
• 22. Lazarev V.N. and Shulenkova T.A. Teoriya i sistemy upravleniya: uchebno-metodicheskoe posobie [Theory and control systems: a teaching aid]. Ulyanovsk, UlSTU, 2022, 168 p. (in Russ.).
• 23. Pulmonology – encyclopedia. Available at: https://medportal.ru/enc/pulmonology (Аccessed: 23 February 2025) (in Russ.).
• 24. Rajpurkar P. Irvin J., Zhu K., et al. Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv preprint, 2017. Available at: https://arxiv.org/abs/1711.05225 (Аccessed: 1 March 2025).
• 25. Yarovoy A.N., Belousova I.M. [The possibilities of computed tomography in the diagnosis of lung diseases]. Radiation diagnostics and therapy, 2020, no. 2, pp. 31–36 (in Russ.).
• 26. Moussavi Z. Respiratory sounds analysis as a diagnostic tool. Medical & Biological Engineering & Computing, 2006, vol. 44, no. 10, pp. 931–933.
• 27. Shaidurova I.A., Vavilova T.V., Kotlyarov P.M. [Acoustic analysis of respiratory noises in lung diseases]. Modern technologies in medicine, 2021, no. 4, pp. 115–121 (in Russ.).
• 28. Yusupova N.I., Nurgayanova O.S., Zulkarneev R.Kh. [Formalization of the stages of risk analysis in SPD, taking into account clinical risk assessments for bronchopulmonary diseases]. System engineering and information technology, 2023, vol. 5, no. 1(10), pp. 11–24 (in Russ.).
• 29. Troshin S.A., Malyshev M.A. [System analysis of doctorpatient interaction in the context of digitalization of healthcare]. Management in healthcare, 2021, no. 3, pp. 32–39 (in Russ.).
• 30. Egorova I.A., Krasilnikova S.M. [Formalization of the patient's condition management model in medical information systems]. Information technologies in medicine, 2020, no. 1, pp. 45–50 (in Russ.).
• 31. Pulmonology – the main diseases. Available at: https://29gkb.ru/about/branches/therapeutic/pulmonology/treateddiseases (Аccessed: 3 June 2025) (in Russ.).