## 3 Results
In this case, one factor has been  
Figure 2: Relation of suPAR and IBI (simple regression, R\({}^{2}\) = 97%, blue line, \(p\) < 0.001). Green dash line–confidence limit. Gray dash line—prediction limit. Note: the expansion of confidence limits and especially prediction in the area of higher suPAR values due to its dependence on patient’s age. extracted (eigenvalue = 1.78). It accounts for 59% of the variability in the original data. Since the principal components method was selected, the initial communality estimates have been set to assume that all of the variability in the data is due to the common factor. Next, rotation was performed in order to simplify the explanation of the factor meaning. The rotated factor (named ALT + Alb + Hb Factor) had the equation:  
\[\text{ALT + Alb + Hb Factor}=0.777927\times\text{ALT + 0.607865}\times\text{Albumin + 0.897814}\times\text{Hb} \tag{2}\]  
where the values of the variables in the equation are standardized by subtracting their means and dividing by their standard deviations. It also shows the estimated communalities, which can be interpreted as estimating the proportion of the variability in each variable attributable to the extracted factor (Figure 4). Figure 4: A three-dimensional space defined by ALT + Alb + Hb Factor, IBIpre and Chlorides Chlorides showing a scattering of dichotomous treatment results (Complications). Figure 3: Preoperative values of the examined markers (CRP—blue line, suPAR—green line and IBI—yellow line) according to the duration of minor complications expressed in days. CRP values are given in mg/L, suPAR values in mg/mL and IBI is an index without unit. Preoperative suPAr and IBI values are weakly related to the duration of postoperative complications. Clinical complication incidents were established as the classification factor (Complications) in a probabilistic neural network (PNN). Input variables were: IBIpre, Chlorides, Glycaemia and ALT + Alb + Hb Factor (meta-variable obtained from the FA). Two neurons were built in summation hidden layer. Among the 74 complete cases in the training set, 74% were correctly classified by the PNN. Results of complication prediction are presented on Figure 5.  
The use of AF as a tool to create an input vector for PNN and the introduction of further input variables containing IBI made 74% of correct treatment results possible to predict (Figure 5). These results of maxilla-facial treatment are understood as an occurrence uneventful outcome or a minor complication. The ability to predict the outcome of treatment with such a method is slightly tilted towards excessive indication of uncomplicated surgical results. Detailed results of PNN predictions indicate that complications should be expected (Figure 6d) in patients with lower preoperative IBI results (i.e., 1.8-2.7) in whom ALT + Alb + Hb_Factor is low (i.e., 65-90) simultaneously. Moreover, these two variables indicate the possibility of complications after surgery when the IBI takes average values (i.e., 2.2-3.4) and ALT + Alb + Hb_Factor is extremely low (i.e., 45-60) and when the IBI is extremely low (i.e., 1.3-1.7) and ALT + Alb + Hb_Factor takes moderately low values (i.e., 66-86). It can also be observed that the frequency of complications increases when the intermediate IBI pre (i.e., 2.3-3.1) in combination with intermediate Glycaemia level (i.e., 4.8-6.5 mmol/L) is present in the patient simultaneously (Figure 6c). The relationship between Chlorides and the occurrence of these minor complications is more complex. In a two-dimensional evaluation with IBI it can be seen a spread of complications from patients with extremely low both Chlorides values (i.e., below 102 mmol/L) and IBI (i.e., below 1.8), up to patients with high Chlorides values (i.e., 106-110 mmol/L) and intermediate IBI values (i.e., 2.3-3.5) (Figure 6b).