## 4 Discussion  
Diabetic retinopathy is an eye disease related to retinal blood vessels. Early detection of diabetic changes prevents impaired vision complications such as diabetic retinopathy and allows appropriate treatment faster. [PERSON] and [PERSON], in a review focusing on KNN and SVM algorithms for the diagnosis of diabetic retinopathy, note that these algorithms are precise and reduce the time of diagnosis in diabetic retinopathy [10]. In our study, the results determined by sDE-SVM are as follows: in the training stage, correct answers were obtained in 100% of the cases, while in the validation stage, a single result was wrong, suggesting that this algorithm is able to provide fairly accurate results (correct answers in 95.23% of cases in the validation stage). This particular case, wrong classified, is for a 9-year-old diabetes patient, a moderate stage glaucoma with increased IOP and, according to the retinopathy changes frequently associated with this type of glaucoma, the classifier considers that the case should have slight modifications associated RD. The classification algorithm provided the answer 1 (slight modifications of RD) and clinical observations indicated 0 (no changes related to RD). By comparing the data obtained in the clinical evaluation with the data resulting from the classification algorithm, a very good correspondence can be observed. These results are comparable to or even better that those reported in the literature on this topic. The optimal structure of the classification algorithm corresponds to a SVM of \(\mu\)-SVM type and to a core function of RBF type. In a recent study, [PERSON] and collaborators used SVM algorithm combined with DE in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **MSE Training** & **MSE Testing** & \multicolumn{4}{c}{**SVM Parameters**} \\ \cline{4-7}  & & & & **C** & \(\gamma\) & \(\
u\) \\ \hline \multirow{8}{*}{**sDE-SVM**} & 1 & 3.368 \(\times\) 10\({}^{-5}\) & 3.066 \(\times\) 10\({}^{-2}\) & 4.8721 & 7.270  
order to classify different types of kidneys based on an analysis of the images obtained through ultrasound [27]. The database they used was limited; therefore, the accuracy of the classification of different types of kidneys was between 70 and 91%. The optimal structures of the SVM classification algorithm were obtained by these authors with DE algorithm by using the cost parameter C \(\epsilon\) [2\(-\)5, 2\(-\)4,..., 25] and the core function parameter \(\gamma\)\(\epsilon\) [2\(-\)5, 2\(-\)4,..., 25]. [PERSON] and [PERSON], in a recent review of the studies available in the literature which used KNN and SVM algorithms in detecting the severity of retinal changes caused by diabetic retinopathy, observed that the results are highly accurate, which could help the ophthalmologist in screening for diabetic retinopathy [10]. [PERSON] and collaborators [28] have recently put forth a screening method, namely, a method of differentiating open-angle glaucoma from glaucoma suspects, using neural networks and models obtained by applying the multiple logistic regression (MLR) method. The accuracy provided by the neural networks was of 84.0%, the sensitivity was of 78.3%, and the specificity was of 85.9%. The use of artificial neural networks can be an effective and low-cost screening method, as well as an effective instrument in differentiating between open-angle glaucoma and glaucoma suspects [28]. To identify changes in diabetic retinopathy, many approaches are based on the analysis of fundus images obtained with high-performance devices [29; 30; 31]. Our study uses SVM and DE algorithms to classify RD changes in patients who have associated two vascular diseases (diabetes and glaucoma) using clinical observations. Thus, the advantage of the simplicity of the procedure must be emphasized, as it is not necessary to acquire images with sophisticated equipment.