DETECTION DIABETIC RETINOPATHY WITH SUPERVISED LEARNING

Abstract

Diabetic retinopathy is a common complication that occurs in people with diabetes mellitus. Diabetic retinopathy damage is characterized in the blood vessel system in the layer at the back of the eye, especially in tissues that respond to light. This research aims to detect diabetic retinopathy early by using SVM and Random forest. SVM is a classification technique that divides the input space into two classes. Random Forest is a supervised learning algorithm that utilizes a collection of decision trees trained using the bagging method. This research uses datasets from diaretdb1 and messidor to evaluate the performance of both methods. The diaretdb1 dataset consists of 178 data points with the diagnosis of Proliferative Diabetic Retinopathy and Non-Diabetic Retinopathy. In addition, the messidor dataset consists of 105 data points with the diagnosis of Diabetic Retinopathy and Non-Diabetic Retinopathy. Experimental results on the diaretdb1 dataset showed that SVM achieved 88% accuracy, while Random Forest achieved 91% accuracy. Similarly, on the messidor dataset, SVM achieved 80% accuracy, while Random Forest achieved 85% accuracy.