Kader, Nur Izzati Ab
(2019)
Hybridization Of Optimized Support
Vector Machine And Artificial Neural
Network For The Diabetic Retinopathy
Classification Problem.
Masters thesis, Universiti Sains Malaysia.
Abstract
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.
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