ellibsЭлектронные библиотекиRussian Digital Libraries Journal1562-5419Казанский (Приволжский) федеральный университет10.26907/1562-5419-2024-27-4-503-532ellibs-560Research ArticleСтатьиРазработка на основе нечеткой логики экспертной системы для диагностики пневмонииDevelopment of an Expert System Based on Fuzzy Logic for Pneumonia DiagnosticsЕникееваАделя ИскандеровнаEnikeevaAdelya Iskanderovnaadisen24@yandex.ruБурнашевРустам АрифовичBurnashevRustam Arifovichr.burnashev@inbox.ruФараховРустам РинатовичFarahovRustam Rinatovichrus-wing-dark@mail.ruКазанский (Приволжский) федеральный университетKazan (Volga region) Federal University202428052025274503532Copyright © Еникеева А.И., Бурнашев Р.А., Фарахов Р.Р., 20252025Еникеева А.И., Бурнашев Р.А., Фарахов Р.Р.Enikeeva A.I., Burnashev R.A., Farahov R.R.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ellibs.elpub.ru/jour/article/view/560

Статья посвящена разработке экспертной системы для диагностики пневмонии. Эта система основана на нечеткой логике и реализована с использованием алгоритма Мамдани. Рассмотрены основные этапы разработки системы, включая фаззификацию входных данных, определение нечетких правил на основе медицинских экспертных знаний, агрегирование нечетких выводов и их дефаззификацию для получения окончательного диагностического результата. Веб-интерфейс системы реализован с использованием фреймворка Django, что обеспечивает удобство взаимодействия для пользователей. Применение медицинской экспертной системы для диагностики пневмонии может сократить время, необходимое для установления диагноза, и улучшить качество диагностики за счет интеграции опыта медицинских экспертов и современных информационных технологий.

The paper is devoted to the development of an expert system for diagnosing pneumonia based on fuzzy logic and implemented using the Mamdani algorithm. The paper discusses the main stages of the system development, including fuzzification of input data, definition of fuzzy rules based on medical expert knowledge, aggregation of fuzzy inferences and their defuzzification to obtain the final diagnostic result. The web interface of the system is implemented using the Django framework, which ensures ease of interaction for users. The use of a medical expert system for diagnosing pneumonia can reduce the time required to establish a diagnosis and improve the quality of diagnosis by integrating the experience of medical experts and modern information technologies.

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The authors declare that there are no conflicts of interest present.