## 5 Conclusions  
It should be noted that the purpose of this study was achieved, and the hypothesis was confirmed. The neural network, preidentified, based on preprocessing initial datasets using suggested methods and algorithms, has acceptable accuracy in a case with a very limited training set. The suggested methodology consisting of interval coding of initial data and search of integrated rating mechanisms, following representation in the form of an artificial neural network and training using initial data, is effective. Based on the results of this study, it should be noted that with the help of special methods based on the mechanism of complex assessment, it was possible to obtain the optimal structure of the neural network model capable of describing the study area with sufficient accuracy, using the example of [PERSON]'s disease. This, once again, confirms that neural network technologies are quite successful in medicine, which is especially valuable with a limited set of initial data. Similar conclusions about the successful application of neural network modeling in medicine and, in particular, the study of the brain, can be found in references [16; 17] etc., where it was proposed to use convolutional neural networks and deep learning models in medicine. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a16050219/s1](https://www.mdpi.com/article/10.3390/a16050219/s1)]([https://www.mdpi.com/article/10.3390/a16050219/s1](https://www.mdpi.com/article/10.3390/a16050219/s1)), Table S1: The training set for identification of integrated rating mechanism (decisions' root). Conceptualization, A.A. and L.K. ; methodology, A.A.; software, L.K. ; validation, A.A., L.K., V.N. and J.B.; formal analysis, A.A., L.K., V.N. and J.B.; investigation, A.A. and L.K. ; resources, L.K. ; data curation, A.A. and L.K. ; writing--original draft preparation, A.A., L.K. and V.N. ;writing--review and editing, A.A., L.K., V.N. and J.B.; visualization, L.K. and J.B.; supervision, A.A.; project administration, L.K. ; funding acquisition, A.A. and L.K. All authors have read and agreed to the published version of the manuscript. This research was carried out with the financial support of the Ministry of Science and Higher Education of the Russian Federation in the framework of the program of activities of the Perm Scientific and Educational Center \"Rational Subsoil Use\". Informed Consent Statement: Not applicable. **Data Availability Statement:** In this study, we used exclusively numerical data given in the appendix to the reference [18]. The computer programs we used by license CC BY-SA 4.0. The computer program RU 2022615135 \"Personal intelligent online development environment \"EIDOS-X Professional\" (System \"EIDOS-Xpro\")\" [55] available at: [[http://lc.kubagro.ru/aidos/_Aidos-X.htm](http://lc.kubagro.ru/aidos/_Aidos-X.htm)]([http://lc.kubagro.ru/aidos/_Aidos-X.htm](http://lc.kubagro.ru/aidos/_Aidos-X.htm)) (accessed on 30 December 2022). The computer program RU 2014618208 \"Neurosimulator 5.0\" [56] available at: [[https://www.lbai.ru/#show/install](https://www.lbai.ru/#show/install)]([https://www.lbai.ru/#show/install](https://www.lbai.ru/#show/install)) (accessed on 30 December 2022). **Acknowledgments:** The authors are grateful to [PERSON] from the Perm National Research Polytechnic University (Perm, Russia) for scientific supervising of the project \"New materials and technologies for medicine\" at the Perm Scientific and Educational Center \"Rational Subsoil Use\"; [PERSON] from the Kuban State Agarian University (Krasnodar, Russia) for free access to the computer program \"EIDOS-Xprofessional\" [55]: [PERSON] from the Perm State University (Perm, Russia) and [PERSON] from the Perm State Humanitarian Pedagogical University (Perm, Russia) for free access to the computer program \"Neurosimulator 5.0\" [56], and also to [PERSON] from V.A.