## 2 Materials and Methods
### Methodology and Methods  
Data preprocessing consists in interval coding of initial data and search of integrated rating mechanisms (decisions' roots). In paper [46] (p. 402), it is shown that any integrated rating mechanism can be represented in the form of an artificial neural network. Thus, it is possible to determine some of the parameters of the required neural network based on the integrated rating mechanism (decision's root). This mechanism reproduces the encoded data using discrete values. This idea corresponds to the approach presented in reference [50]. The scientific hypothesis lies in the assumption that it will be possible to obtain the acceptable accuracy of the neural network by training a predefined neural network on the initial data in a continuous form. According to [46] (p. 398) the integrated rating mechanism is determined as a tuple (1). \[<\mathrm{G},\mathrm{M},\mathrm{X},\mathrm{P}>, \tag{1}\]  
where G is a graph describing a sequence of convolution of particular criteria, M is a set of convolution matrices corresponding to the nodes of a criteria tree, X is a set of the scales for \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Subject** & **Group 1** & **L\_BV** & **R\_BV** & **L\_ICV** & **R\_ICV** & **L\_TV** & **R\_TV** & **L\_SV** & **R\_SV** & **L\_DNV** & **R\_DNV** \\ \hline sub001 & 1 & 279 & 288 & 255 & 263 & 140 & 138 & 131 & 165 & 185 \\ sub002 & 1 & 274 & 247 & 223 & 243 & 239 & 262 & 190 & 222 & 204 & 102 \\ sub003 & 1 & 259 & 333 & 236 & 243 & 172 & 159 & 135 & 145 & 152 & 153 \\... &... &... &... &... &... &... &... &... &... &... &... \\ sub079 & 0 & 279 & 288 & 179 & 189 & 141 & 153 & 245 & 214 & 177 & 149 \\ sub080 & 0 & 249 & 232 & 221 & 232 & 154 & 142 & 165 & 151 & 161 & 143 \\ sub081 & 0 & 300 & 259 & 295 & 299 & 216 & 197 & 163 & 131 & 162 & 129 \\ \hline \hline \end{tabular}  
* In this Table in column “Group” 1 is the AD group, and 0—the CON group.  
\end{table} 
Table 1: The fragment of the initial data with magnetic susceptibility values of the cerebral veins [18]. the scoring of the particular criteria, and P is the procedure of aggregation. In this study, discrete procedure is used, but continuous, interval, fuzzy and F-fuzzy procedures are also noted [51]. One of the problems of identifying integrated rating mechanisms (decisions' roots) is the choice of the structure of the graph G, which is a full binary tree with labeled leaves [52], because their total number is determined according to the equation [43; 48]:  
\[|\text{G}|=(2l-3)!!, \tag{2}\]  
where \(l\) is the total number of considered variables (leaves in terms of tree graph). The number of G structures for 4 variables is 15, for 5-105, for 6-945 as can be seen from Equation (2), and there are already more than 34 million, for example, for 10 variables. This is a key limitation of the proposed algorithms, as can be seen from the above example. To reduce the number of variables, it is suggested to use methods of the system-cognitive analysis (hereinafter-SCA) [53; 54] using the computer program RU 2022615135 \"Personal intelligent online development environment \"EIDOS-X Professional\" (System \"EIDOS-Xpro\")\" [55], developed by professor [PERSON], [PERSON] In this computer program, to solve the classification problem, two integrated similarity criteria are used, known as \"Sum of knowledge\" (3) and \"Semantic resonance of knowledge\" (4). \[I_{j}=\sum_{i=1}^{I}L_{i}I_{ij},\quad L_{i}=\{0;1\}, \tag{3}\]  
where \(I_{j}\)--is the amount of information about class \(j\); \(I\)--the amount of features (signs, attributes); \(L_{i}\)--a variable describing the presence (\(L_{i}=1\)) or absence (\(L_{i}=0\)) of the feature \(i\); \(I_{ij}\)--assessment of informational importance of feature \(i\) for class \(j\) (amount of information about feature \(i\) for class \(j\)).