Hybridization Of Optimized Support Vector Machine And Artificial Neural Network For The Diabetic Retinopathy Classification Problem - Kader, Nur Izzati Ab (2019)
Diabetic Retinopathy (DR) is one of the most threatening disease which caused blindness for diabetic patient. With the increasing number of DR cases nowadays, diabetic eye screening has become a challenging task for ophthalmologist as they need to deal with a large number of retinal image to be diagnosed every day. Screening and early detection of DR play a vital role to help reducing the incidence of visual morbidity and vision loss. The screening task is done manually in most countries using qualitative scale to detect abnormalities on the retina. Although this approach is useful, the detection is not accurate. Previous researchers have tried a few attempts to propose an automatic DR classification, however it needs to be improvised especially in terms of accuracy. A group of literates showed that DR classification can be performed using the clinical features resulted from the blood test such as glycated haemoglobin, triglyceride, creatine and glucose value. Even this subject have been studied previously, but it remains the subject of on-going research. Hence, this research aims to obtain optimal or near-optimal performance value in the study of diabetic classification using supervised machine learning. There are many algorithms available for classification purpose such as k-Nearest Neighbour, k-Means, Support Vector Machine, Decision Tree, Artificial Neural Network and Linear Discriminant Analysis. Due to the success of many classification problems been proposed with good result, k-Nearest Neighbour, Artificial Neural Network, and Support Vector Machine algorithms are used in this research.