vitjВрач и информационные технологииMedical Doctor and Information Technologies1811-01932413-5208Pirogov National Medical and Surgical Center10.25881/18110193_2025_4_28vitj-272Research ArticleОБЗОРЫREVIEWSНечёткие модели в задаче дескриптивного и предиктивного анализа медицинских данных пациентов с хроническими неинфекционными заболеваниямиFuzzy models in descriptive and predictive analysis of medical data of patients with chronic non-communicable diseasesАфанасьеваТ. В.AfanasevaT. V.

д.т.н., доцент

г. Москва

DSc, Associate Professor

Moscow

tv.afanasjeva@gmail.comРЭУ им. Г. В. ПлехановаРоссияPlekhanov Russian University of EconomicsRussian Federation202523122025042843Copyright © Афанасьева Т.В., 20252025Афанасьева Т.В.Afanaseva T.V.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.vit-j.ru/jour/article/view/272

В связи с ростом продолжительности жизни увеличивается население, в том числе трудоспособного возраста, с хроническими неинфекционными заболеваниями (ХНЗ), что повышает нагрузку на медицинский персонал. В это связи возрастает потребность в автоматизированной обработке медицинских данных, особенно данных, поступающих из систем мониторинга ключевых показателей здоровья пациентов с ХНЗ. Ключевыми задачами в этом направлении являются точное описание текущего состояния здоровья пациента и своевременная диагностика заболевания. Решение этих задач имеет первостепенное значение для эффективного ведения пациентов с ХНЗ, обеспечивая поддержку врачей в выборе оптимальной стратегии лечения. При решении указанных задач нечеткие модели имеют большой потенциал ввиду нечеткости медицинских данных, возможности моделировать знания медицинских специалистов и низкой вычислительной сложности. В статье рассмотрены, систематизированы и обобщены результаты 29 исследований, опубликованных в период с 2015 по 2025 год, посвященных задачам дескриптивного и предиктивного анализа числовых медицинских данных пациентов с ХНЗ на основе нечетких моделей, использующих нечеткие множества и нечеткий логический вывод. Особое внимание уделено оценке точности нечетких моделей для различных ХНЗ. Анализ публикаций демонстрирует конкурентоспособность и высокую эффективность нечетких моделей в анализе данных, о чем свидетельствуют метрики точности (от 90% до 99,61%) и чувствительности (от 80,94% до 98,57%), за исключением исследований, посвященных онкологическим заболеваниям. Полученные результаты могут послужить основой для разработки систем поддержки принятия врачебных решений.

As life expectancy increases, the number of people, including those of working age, with chronic noncommunicable diseases (CNCDs), increasing the workload of healthcare personnel, is growing. This increases the need for automated processing of medical data, particularly data from monitoring systems for key health indicators of patients with CNCDs. Key tasks in this area include accurately describing the patient's current health status and timely diagnosis. Addressing these challenges is crucial for the effective management of patients with CNCDs, supporting physicians in choosing the optimal treatment strategy. Fuzzy models offer significant potential for solving these problems due to the ambiguity of medical data, the ability to model the knowledge of medical professionals, and their low computational complexity. This article reviews, systematizes, and summarizes the results of 29 studies published between 2015 and 2025 on the descriptive and predictive analysis of numerical medical data from patients with CNDs using fuzzy models employing fuzzy sets and fuzzy logical inference. Particular attention is paid to assessing the accuracy of fuzzy models for various CNDs. The analysis of publications demonstrates the competitiveness and high efficiency of fuzzy models in data analysis, as evidenced by accuracy (from 90% to 99.61%) and sensitivity (from 80.94% to 98.57%) metrics, with the exception of cancer studies. The obtained results can serve as a basis for the development of medical decision support systems.

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