Mathematical Models and Machine Learning Algorithms in the Diagnosis of Complications of Type 1 Diabetes Mellitus

Abstract

The paper deals with the problem of diabetic polyneuropathy diagnosing. This is one of the earliest and most dangerous complications of diabetes among children and adolescents. The research aims to develop models for diagnosing diabetic polyneuropathy in children and adolescents based on various medical data. The developed models will make it possible to diagnose a complication without using neurophysiological research methods. Therefore, the proposed models can be used in small medical and obstetrical stations in rural areas as well as a support system for making medical decisions. In the course of the study, a review and analysis of scientific publications of domestic and foreign scientists on the topic of the research are carried out. A large set of textual medical data is processed, then a database is created, features are analyzed, and a model is developed to reveal the presence of diabetic polyneuropathy in children and adolescents with type 1 diabetes mellitus. The achieved quality of the classification model allows us to assert that machine learning methods can be used to find hidden dependencies in the development and course of complications of diabetes mellitus.