APPLICATION OF FUZZY LOGIC IN PROCESSING THE RESULTS OF MEDICAL RESEARCH

Abstract

The thesis presents information technology for processing medical indicators using fuzzy logic. The application of such technology to the distribution of endocrinological parameters makes it possible to assess the correctness of decision-making and the probability of false conclusions. Of the 29 endocrinological indicators, four key ones that have the greatest impact on the final result were identified: body mass index (BMI), total vitamin D3, total cholesterol, and fasting blood glucose. The concave curve rule was used to calculate probabilities, which allows for a more accurate assessment of risks and the reliability of conclusions.

The practical implementation of the use of terms of fuzzy logic when comparing two ways of treating hypertension and obesity is shown. The research was carried out at the clinical bases of the Department of Family Medicine and Outpatient Polyclinic Care of P. L. Shupyka. Patients with hypertension and obesity were divided into two groups, randomized by age, sex, and comorbid pathology, who were given two types of treatment: the main group (M2) received treatment 1, the experimental group (M3) received treatment 2. The thesis investigates the optimization of complex therapy and diagnosis of patients with arterial hypertension and obesity in primary medical practice and the establishment of interrelationships between different treatment methods and confirmation of the effectiveness of treatment using terms of fuzzy logic. The study confirmed the importance of accounting for individual physiological characteristics. Results indicate that endocrinological indicators are highly individual: values considered normal for one person may be critical for another. This underscores the necessity of a personalized approach in diagnosing and treating endocrine disorders. The influence of measurement errors on analysis accuracy was also noted, necessitating further methodological improvements. Future research in this field will enhance diagnostic quality and the effectiveness of medical decisions.