{"references": ["[1]. \u0411\u0430\u0440\u0441\u043a\u0438\u0439, \u0410.\u0411. \u041b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0435 \u0441\u0435\u0442\u0438: \u0423\u0447\u0435\u0431\u043d\u043e\u0435 \u043f\u043e\u0441\u043e\u0431\u0438\u0435 - \u041c.: \u0411\u0438\u043d\u043e\u043c, 2013. - 352 c. [2]. \u0413\u0430\u043b\u0443\u0448\u043a\u0438\u043d, \u0410.\u0418. \u041d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0435 \u0441\u0435\u0442\u0438: \u043e\u0441\u043d\u043e\u0432\u044b \u0442\u0435\u043e\u0440\u0438\u0438. / \u0420\u0438\u0421, 2014. - 496 c. [3]. \u0420\u0435\u0434\u044c\u043a\u043e, \u0412.\u0413. \u042d\u0432\u043e\u043b\u044e\u0446\u0438\u044f, \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0435 \u0441\u0435\u0442\u0438, \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442: \u041c\u043e\u0434\u0435\u043b\u0438 \u0438 \u043a\u043e\u043d\u0446\u0435\u043f\u0446\u0438\u0438 \u044d\u0432\u043e\u043b\u044e\u0446\u0438\u043e\u043d\u043d\u043e\u0439 \u043a\u0438\u0431\u0435\u0440\u043d\u0435\u0442\u0438\u043a\u0438 / \u041b\u0435\u043d\u0430\u043d\u0434, 2015. - 224 c. [4]. \u0410\u0440\u0430\u0432\u0438\u043d \u041e.\u0418. \u041f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0445 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0445 \u0441\u0435\u0442\u0435\u0439 \u0434\u043b\u044f \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u043f\u0430\u0442\u043e\u043b\u043e\u0433\u0438\u0439 \u0432 \u043a\u0440\u043e\u0432\u0435\u043d\u043e\u0441\u043d\u044b\u0445 \u0441\u043e\u0441\u0443\u0434\u0430\u0445. \u2013 \u0410\u0441\u0442\u0440\u0430\u0445\u0430\u043d\u044c: \u041c\u0435\u0434\u0438\u0446\u0438\u043d\u0430 \u0438 \u0437\u0434\u0440\u0430\u0432\u043e\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u0438\u0435. 2011. \u0441. 45-51. [5]. Specht D. A. General Regression Neural Network. \u2013 IEEE Trans.: on Neural Networks. 1991. \u0441. 568-576. [6]. \u0412.\u0410. \u0414\u044e\u043a, \u0412.\u0410. \u0421\u0430\u043c\u043e\u0439\u043b\u0435\u043d\u043a\u043e \u0418\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0435 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438 \u0432 \u043c\u0435\u0434\u0438\u043a\u043e-\u0431\u0438\u043e\u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f\u0445 / \u041f\u0438\u0442\u0435\u0440. 2001. \u2013 368 \u0441. [7]. \u0413\u0430\u043b\u0443\u0448\u043a\u0438\u043d \u0410. \u041d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0435 \u0441\u0435\u0442\u0438. \u041e\u0441\u043d\u043e\u0432\u044b \u0442\u0435\u043e\u0440\u0438\u0438. / \u0413\u043e\u0440\u044f\u0447\u0430\u044f \u043b\u0438\u043d\u0438\u044f\u0442\u0435\u043b\u0435\u043a\u043e\u043c. 2012. \u2013 253 \u0441. [8]. \u041e\u0441\u043d\u043e\u0432\u044b \u0442\u0435\u043e\u0440\u0438\u0438 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0445 \u0441\u0435\u0442\u0435\u0439 / \u0415.\u0412. \u0411\u043e\u0434\u044f\u043d\u0441\u043a\u0438\u0439, \u041e.\u0413. \u0420\u0443\u0434\u0435\u043d\u043a\u043e \u2013\u041c.: \u0412\u044b\u0441\u0448\u0430\u044f \u0448\u043a\u043e\u043b\u0430. 2003. \u2013 317 \u0441. [9]. \u0410.\u0418. \u0413\u043e\u043b\u0443\u0448\u043a\u0438\u043d, \u0410.\u0412. \u0428\u043c\u0438\u0434. \u041e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b \u043c\u043d\u043e\u0433\u043e\u0441\u043b\u043e\u0439\u043d\u044b\u0445 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0445 \u0441\u0435\u0442\u0435\u0439 // \u041d\u0435\u0439\u0440\u043e\u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440, \u21162. 1992. \u0441. 7-11. [10]. Abdumanonov A.A., Xalilov D.A., Jumaboyeva N.A. Research of methods of application of neuroinformation networks in medicine // Scientific ideas of young scientists / Pomysly naukowe mlodych naukowcow International scientific and practical conferences January, 2021 Warsaw, Poland 53p. [11]. Abdumanonov A.A., Xalilov D.A., Jumaboyeva N.A. \u041d\u0435\u0439\u0440\u043e \u0441\u0435\u0442 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u043c\u0435\u0434\u0438\u0446\u0438\u043d\u0441\u043a\u0438\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0438 \u043f\u0440\u0438\u043d\u044f\u0442\u0438\u0438 \u0432\u0440\u0430\u0447\u0435\u0431\u043d\u044b\u0445 \u0440\u0435\u0448\u0435\u043d\u0438\u0438 // \"\u0422\u0438\u0431\u0431\u0438\u0451\u0442 \u0430\u0445\u0431\u043e\u0440\u043e\u0442 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u044f\u043b\u0430\u0440\u0438\u043d\u0438\u043d\u0433 \u0440\u0438\u0432\u043e\u0436\u043b\u0430\u043d\u0438\u0448 \u0438\u0441\u0442\u0438\u049b\u0431\u043e\u043b\u043b\u0430\u0440\u0438\" \u043c\u0430\u0432\u0437\u0443\u0441\u0438\u0434\u0430 \u0420\u0435\u0441\u043f\u0443\u0431\u043b\u0438\u043a\u0430 \u0438\u043b\u043c\u0438\u0439-\u0430\u043c\u0430\u043b\u0438\u0439 \u043e\u043d\u043b\u0430\u0439\u043d \u0430\u043d\u0436\u0443\u043c\u0430\u043d\u0438 \u0442\u045e\u043f\u043b\u0430\u043c\u0438 \u0424\u0430\u0440\u0493\u043e\u043d\u0430 2021 \u0439 17-23 \u0431. [12]. \u0410\u0431\u0434\u0443\u043c\u0430\u043d\u043e\u043d\u043e\u0432 \u0410.\u0410., \u0425\u0430\u043b\u0438\u043b\u043e\u0432 \u0414.\u0410., \u0416\u0443\u043c\u0430\u0431\u043e\u0435\u0432\u0430 \u041d. \u0410. \u0422\u0438\u0431\u0431\u0438\u0451\u0442\u0434\u0430 \u0441\u0443\u043d\u0438\u0439 \u043d\u0435\u0439\u0440\u043e\u0442\u0430\u0440\u043c\u043e\u049b\u043b\u0430\u0440 // \u0418\u043d\u043d\u043e\u0432\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0435 \u043f\u043e\u0434\u0445\u043e\u0434\u044b \u0438 \u0430\u043d\u0442\u0443\u0430\u043b\u044c\u043d\u044b\u0435 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b \u043f\u0440\u0435\u043f\u043e\u0434\u0430\u0432\u0430\u043d\u0438\u044f \u0444\u0443\u043d\u0434\u0430\u043c\u0435\u043d\u0442\u0430\u043b\u044c\u043d\u044b\u0445 \u0434\u0438\u0441\u0446\u0438\u043f\u043b\u0438\u043d \u041c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u044b"]}

This article presents an analysis of the use of artificial neural network technologies in the medical diagnosis of diseases, the purpose of which is to determine which areas of diagnosis using neural network technologies are the most effective, as well as the effectiveness of learning system algorithms. At the same time, the structure of artificial neural networks, learning algorithms and the accuracy of the functioning of artificial neural networks were considered. In the article it was found that the most optimal model of artificial neural networks for solving problems of medical diagnostics is a multilayer perceptron, which is a direct propagation network in which neurons of one layer are sequentially connected to neurons of adjacent layers without recurrent connections, it was revealed that the most optimal algorithms for training a multilayer perceptron are an error back propagation algorithm and a genetic algorithm. The introduction of neural networks of diagnostic models into clinical practice can provide effective assistance in making medical decisions, improve the quality and accuracy of diagnosis of diseases