OPTIMIZATION MODELING OF INTELLIGENT DIAGNOSTIC SYSTEMS

Abstract

This article considers the actual task of developing optimization models for intelligent diagnostic systems, which is important for increasing the efficiency of diagnostic processes in various industries. The article focuses on the study and systematization of modern methods of mathematical modeling and algorithms of intelligent data analysis, which are used to optimize decision-making in diagnostic systems. The relevance of the topic lies in the need to integrate the latest technologies of data analysis and mathematical modeling to increase the accuracy, speed and efficiency of diagnostic systems. As part of the research, optimal models have been created that allow us to effectively take into account the available resources and tools needed to solve specific diagnostic problems. The models integrate the main elements for efficient allocation of resources and meeting the demands of experts and define a set of diagnostic parameters together with a set of means to measure them. Special attention is paid to the adaptation of these models for the specifics of various diagnostic tasks, including the integration of intelligent components, such as machine learning and artificial intelligence, which contribute to increasing the accuracy of diagnoses and optimizing processes. The integration of machine learning algorithms and artificial intelligence into the model involves the use of an efficiency matrix that reflects the degree of feasibility of using the proposed algorithms to solve certain diagnostic problems. Modeling technology of intelligent diagnostic systems has been developed taking into account these models, which demonstrates practical applicability and testing capabilities in real conditions. A significant amount of work is devoted to testing the developed models through the implementation of the "Optimization modeling of medical diagnosis" software tool. This tool not only allows testing models in controlled conditions, but also ensures their adaptation to real diagnostic scenarios, significantly increasing the practical value of research. It provides calculation of model parameters, their visualization in a convenient format and the possibility of quick adjustments depending on the specifics of the task.