Type I diabetes, also known as insulin-dependent diabetes, is a serious disease. There are two main approaches to the management of patients with this disease: the use of insulin pens and various types of insulin (such as ultrashort, short-acting and long-acting insulins) or the use of an insulin pump with ultrashort insulin. An important task remains the development of a reliable system for the accurate calculation of insulin doses using insulin pens in the treatment of type I diabetes. In this study, we investigate the accuracy of calculating insulin doses based on historical data. To solve this problem, we propose using various artificial intelligence (AI) methods, including decision trees, gradient acceleration, support vector machine (SVM) methods and various neural network architectures. We are conducting a comparative analysis of these approaches to determine their
effectiveness.
Keywords:
type I diabetes, machine learning, neural networks
