Synthesis of Artificial Neural Networks Using a Modified Genetic Algorithm Serhii Leoshchenko 1[0000-0001-5099-5518] , Andrii Oliinyk 2[0000-0002-6740-6078] , Sergey Subbotin 3[0000-0001-5814-8268] , Nataliia Gorobii 4[0000-0003-2505-928X] and Tetiana Zaiko 4[0000-0003-1800-8388] 1,2,3,4,5 Zaporizhzhia National Technical University, Dept. of Software Tools, 69063 Zaporizhzhia, Ukraine 1 sedrikleo@gmail.com, 2 olejnikaa@gmail.com, 3 subbotin@zntu.edu.ua 4 gorobiy.natalya@gmail.com, 5 nika270202@gmail.com Abstract. This paper is devoted to the complex problem of synthesis of artifi- cial neural networks. Firstly, the existing methods, recommendations and solu- tions of the problem are consider. As a new solution, the mechanism of using the modification of the genetic algorithm to determine the weights of the hidden and output layers (network training) is proposed. Testing and comparison of the results with the results of the existing methods were carried out for the correct evaluation of the method. Keywords: artificial neural networks, synthesis, network training, recurrent connections, genetic algorithm, mesothelioma. 1 Introduction In modern medicine, the main task for the use of information technology is to signifi- cantly improve the quality indicators in the diagnosis and therapy of various diseases. The existing methods and algorithms of modeling nonlinear systems are faced with problems of high dimensionality of tasks, the requirements of high accuracy and gen- eralizing ability of the obtained models. These problems can be solved with the help of per-boron and iterative methods, which are based on the principles of selection, evolution and adaptation, which are methods of heuristic self-organization. At the same time, in real life it is quite difficult to create an adequate model of a complex object using only one method of inductive modeling. Usually it is required to combine modern methods and technologies of heuristic self-organization, to apply multilevel modeling, to develop hybrid algorithms. Insufficiency or overabundance of data are frequent problems in solving tasks: there is not enough experiments (in the modeling of physical objects), not enough or difficult to allocate informative data on patients (to build a prediction of health). It is necessary to determine the parameters of the new element, to predict the outcome of the disease, to recommend treatment. Often artificial neural networks (ANNs) are used to solve such problems.