Classification of diabetic retinopathy disease from Video-Oculography (VOG) signals with feature selection based on C4.5 decision tree

“Diabetes Mellitus (Diabetes)” is a disease based on insulin hormone disorders secreted from the pancreas gland. Clinical findings find out that diabetes causes some diseases in vital organs. “Diabetic Retinopathy” is one of the most common eye diseases based on diabetes, and it is the leading cause of visual loss resulting from structural changes in the retinal vessels. Recent researches show that signals from vital organs can be used to diagnose diseases in the literature. In this study, the features of horizontal and vertical Video-Oculography (VOG) signals from right and left eye are used to classify non-proliferative and proliferative diabetic retinopathy disease. 25 statistical features are obtained using discrete wavelet transform with VOG signals from 24 subjects. Feature selection is performed using C4.5 decision tree algorithm from 25 features obtained. The statistical features obtained from C4.5 decision tree and discrete wavelet transform are applied as input to artificial neural networks and the classification performance of the “Diabetic Retinopathy” disease are compared according to these two methods. Our results show that feature selection by C4.5 decision tree algorithm (96.87%) provides better classification performance than feature extraction with discrete wavelet transform (93.75%).