Intelligent Support for Decision-Making
Abstract
This article presents the development and study of a model for formalizing the diagnosis process using artificial intelligence methods. At present, various artificial neural networks and expert systems have been created and are used in the diagnosis.
The analysis of these works showed that these methods show good results, but they have a number of disadvantages, the most significant of which is the complexity of the organization and the large time spent on training the neural network. Thus, the problem is posed of developing new algorithms that have the probability of making an accurate diagnosis, comparable to artificial neural networks and expert systems, and at the same time having less training time. One of the ways to solve this problem is to develop a model for diagnosing diabetes mellitus based on an artificial immune system. The aim of the work is to develop and study a model for formalizing the diagnosis process using artificial intelligence methods. A model of the diagnosis process is considered: pre-diabetes state (impaired glucose tolerance, impaired fasting glucose), type I diabetes, type II diabetes.
The problem of diagnosing a disease can be viewed as a classification problem. In this work, the process of making a diagnosis was considered as dividing these analyzes and anamnesis of patients into four classes corresponding to one of the diagnoses: pre-diabetes state (impaired glucose tolerance, impaired fasting glycemia), type I diabetes, and type II diabetes.
To solve this problem, an artificial immune system and an artificial neural network of Kohonen were used.
An artificial immune system represents an idealized version of a natural analogue and reproduces the key components of a natural process: selection of the best antibodies in a population depending on the degree of their affinity (proximity) to an antigen, cloning of antibodies, and mutation of antibodies.
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