## 3 Results  
### Results of the Preliminary Data Analysis  
The initial data were processed using SCA methods. The intervals of magnetic susceptibility values of the above-described veins L_BV, R_BV, L_ICV, R_ICV, L_TV, R_TV, L_SV, R_SV, L_DNV, R_DNV were used as features. The state of the patient was used as a class: AD group or CON group, and the domains of observed values were divided into Three equal intervals for each vein (Table 2). Such a division of the initial dataset into three intervals was due to expert opinion, since when choosing two intervals, we get the classical problem of finding a Boolean function. The choice of four intervals is  
Figure 1: Suggested algorithm of data preprocessing and neural network architecture selection for neural network training in limited initial dataset cases. inappropriate, since when using 10 variables in the initial data, 80 features will be formed, and the initial dataset, which must be divided into training and testing, contains only 81 examples. Of interest is the division of the observation area into different intervals with an almost uniform number of examples in each of these intervals. In view of the multivariate results of possible partitions, this approach will be considered in a future study. With the help of the intelligent analytical system \"EIDOS-Xpro\", all patients were distributed based on the level of progression of [PERSON]'s disease by applying two integral similarity criteria: \"Semantic resonance of knowledge\" (4) and \"Sum of knowledge\" (3) (Figure 2). The intervals of MSV were determined as a result of the application of SCA. These values had the greatest informational significance for classification of a patient in the AD group (Figure 3, see \"contributing\"), as well as signs that did not characterize this class to the greatest extent (Figure 3, see \"impending\"). Since patients belong to only two classes (AD and CON) in the research dataset, the insignificance for the AD group meant significance for the CON group, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cerebral Veins** & **Variable Abbrev.** & **Domain of MSV** & **1 st Interval** & **2 nd Interval** & **3 rd Interval** \\ \hline left basal vein & L\_BV & (153.0; 324.0) & (153.0; 210.0) & (210.0; 267.0) & (267.0; 324.0) \\ right basal vein & R\_BV & (164.0; 357.0) & [164.0; 228.3] & [228.3; 292.7] & [292.7; 357.0] \\ left internal cerebral vein & L\_ICV & (179.0; 394.0) & (179.0; 250.7) & [250.7; 322.3] & [322.3; 394.0] \\ right internal cerebral vein & R\_ICV & [163.0; 411.0] & [163.0; 245.7] & [245.7; 328.3] & [328.3; 411.0] \\ left vein of the thalamus & L\_TV & (131.0; 288.0) & (131.0; 183.3) & [183.3; 235.7] & [235.7; 288.0] \\ right vein of the thalamus & R\_TV & (109.0; 286.0) & [109.0; 168.0] & [168.0; 227.0] & [227.0; 286.0] \\ left septal vein & L\_SV & (83.0; 310.0) & (83.0; 158.7) & (158.7; 234.3) & [234.3; 310.0] \\ right septal vein & R\_SV & (73.0; 287.0) & [73.0; 144.3] & [144.3; 215.7] & [215.7; 287.0] \\ left vein of the dentate nucleus & L\_DNV & (94.0; 244.0) & (94.0; 144.0) & (144.0; 194.0) & [194.0; 244.0] \\ right vein of the dentate nucleus & R\_DNV & (81.0; 269.0) & (81.0; 143.7) & [143.7; 206.3] & [206.3; 269.0] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The intervals for discrete coding. Figure 2: Recognition results for the AD group of patients in the “Class-objects” mode in the system “EIDOS-Xpro”. It can be seen from the left part of Figure 3, that the 3 rd interval of MSV in the veins of the thalamus (L_TV, R_TV) are among the first 3 most contributing factors, and the 1 st interval of MSV in the same veins are impending factors (see right part of Figure 3). Second and third intervals of MSV for both the basal veins (R_BV, L_BV) were included in seven contributing factors for classification of a patient in the AD group (see Figure 3), and the first interval of MSV in both left and right basal veins are considered impending factors (see Figure 3) [40].