## 3 Results  
The study was performed on a sample of 52 patients: more particularly, 101 eyes with POAG + DM; and the effectiveness of the classification algorithm in assessing retinal changes related to diabetes is proven. The mean age was 64.9 \(\pm\) 7.06 (between 48 and 74 years old). The distribution by age groups highlights the fact that the most increased frequency of patients with diabetic retinopathy changes can be found in the age group 70-79 years (50%), while for other patients, the frequency peak is reached in the group 60-69 years (47.2%) (Chi-square distribution = 41.93; degrees of freedom--df = 3; probability--_p_ = 0.001). From all studied eyes, 72 eyes (71.28%) had POAG, 21 eyes (20.8%) had OHT and 8 eyes (7.92%) had normal tension glaucoma (NTG). Among patients with diabetes mellitus, 30 had DM type II, 5 had DM type I and 16 with insulin--dependent DM type II (Table 1). Among patients with diabetic retinopathy changes, the age of glaucoma was significantly increased in comparison with the control group (5.67 vs. 3.97 years; \(p\) = 0.049). In the studied cases, patients with diabetic retinopathy were predominant along with patients with glaucoma for 5-10 years (50%), out of which 1/3 were men and 1/3 aged over 65 years (_p_ = 0.001). Regarding the changes in diabetic retinopathy, 5.94% had mild changes, 4.95% had moderate/severe DR and the rest (89.11%) did not show any changes (Figure 2). \begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **G + DM** \\ \hline Ages & 64.9 \(\pm\) 7.06 \\ Sex & 74.13\%F, 25.87\%M \\ Glaucoma duration & 3.65 \(\pm\) 3.06 \\ Diabetes duration & 7.92 \(\pm\) 6.12 \\ Type diabetes & \\ I & 9.81\% \\ II & 58.92\% \\ MIXT & 31.37\% \\ HgA1c & 6.98 \(\pm\) 1.24 \\ Glycemic level & 137.42 \(\pm\) 23.17 \\ Without DR 89.11\% \\ DR changes & Mild DR 5.94\% \\ Moderate/severe DR 4.95\% \\ \hline \hline \end{tabular} DM = diabetes mellitus; HgA1c = glycosylated hemoglobin; G = glaucoma; DR = diabetic retinopathy; type diabetes; I = insulin dependent, II = medical treatment, Mixt = medical and insulin dependent, Glaucoma duration = \"time since first diagnosis\", diabetes durations = \"time since first diagnosis\".  
\end{table} 
Table 1: Characteristics of the patients included in the study. After the data were gathered and processed, the sDE-SVM approach was applied in order to classify the output (DR) into 3 groups. As previously mentioned, the output parameter had 3 values (0--lack of modifications, 1--small modifications and 2--moderate or severe DR modifications). In the classical SVM application, for a non-binary classification, the output should be encoded using a binary approach and three different classifiers should be determined [26]. However, in the current work, due to the simple workflow considered, the recommendation was not applied and the output remained coded in decimal form. In order to determine the performance of the sDE-SVM algorithm, a set of 10 simulations were performed, the results being provided in Table 2. Here, C and \(\
u\) are the parameters of the SVM optimization problem and \(\gamma\) is the parameter of the kernel function. In addition, a comparison between sDE-SVM and DE-SVM is performed, the results being presented comparatively. Prior to performing the simulations, in order to determine the performance of the classifier on previously unseen data, the available dataset was split into 75% training and 25% testing. This allocation was performed in a random manner to ensure that both sub-sets cover the entire data domain. The parameters taken into account for optimization were: type of SVM (\(\epsilon\)-SVM, \(\mu\)-SVM), type of center function (linear, polynomial, basic radial function--RBF, and sigmoid), the degree of the polynomial (only for polynomial type of the center function), C, \(\
u\), \(\gamma\) (for RBF and polynomial type of the centre function), d (for polynomial type of the center function), r (for polynomial type of the center function).