The effect of generalized discriminate analysis (GDA) to the classification of optic nerve disease from VEP signals

摘要: In this paper, we have investigated the effect of generalized discriminate analysis (GDA) on classification performance optic nerve disease from visual evoke potentials (VEP) signals. The GDA method has been used as a pre-processing step prior to process disease. proposed consists two parts. First, increase distinguishing VEP Second, C4.5 decision tree classifier, Levenberg Marquart (LM) back propagation algorithm, artificial immune recognition system (AIRS), linear discriminant (LDA), and support vector machine (SVM) classifiers. Without GDA, obtained 84.37%, 93.75%, 75%, 76.56%, 53.125% accuracies using LM AIRS, LDA, SVM algorithms, respectively. With 93.86%, 81.25%, 93.75% above These results show that produced very promising in diagnosis