Neural computing: an introduction
Neural computing: an introduction
The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Training a learning vector quantization network using the pattern electroretinography signals
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Neural Network-Based Diagnosing for Optic Nerve Disease from Visual-Evoked Potential
Journal of Medical Systems
Computers in Biology and Medicine
Visual evoked potentials discrimination based on adaptive zero-tracking neural network
Computers in Biology and Medicine
Utilization of artificial neural networks in the diagnosis of optic nerve diseases
Computers in Biology and Medicine
Computers and Electrical Engineering
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The optic nerve disease is an important disease that appears commonly in public. In this paper, we propose a hybrid diagnostic system based on discretization (quantization) method and classification algorithms including C4.5 decision tree classifier, artificial neural network (ANN), and least square support vector machine (LSSVM) to diagnose the optic nerve disease from Visual Evoked Potential (VEP) signals with discrete values. The aim of this paper is to investigate the effect of Discretization method on the classification of optic nerve disease. Since the VEP signals are non-linearly-separable, low classification accuracy can be obtained by classifier algorithms. In order to overcome this problem, we have used the Discretization method as data pre-processing. The proposed method consists of two phases: (i) quantization of VEP signals using Discretization method, and (ii) diagnosis of discretized VEP signals using classification algorithms including C4.5 decision tree classifier, ANN, and LSSVM. The classification accuracies obtained by these hybrid methods (combination of C4.5 decision tree classifier-quantization method, combination of ANN-quantization method, and combination of LSSVM-quantization method) with and without quantization strategy are 84.6-96.92%, 94.20-96.76%, and 73.44-100%, respectively. As can be seen from these results, the best model used to classify the optic nerve disease from VEP signals is obtained for the combination of LSSVM classifier and quantization strategy. The obtained results denote that the proposed method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system.