Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.cmpb.2008.04.009 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
hybrid method,vep signal,discretization method,decision tree classifier,lssvm-quantization method,decision tree classifier-quantization method,optic nerve disease,ann-quantization method,quantization strategy,hybrid system,hybrid systems,least squares support vector machine,artificial neural network | Decision tree,Discretization,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Artificial neural network,Statistical classification,Classifier (linguistics),Quantization (signal processing),Decision tree learning | Journal |
Volume | Issue | ISSN |
91 | 3 | 0169-2607 |
Citations | PageRank | References |
7 | 0.50 | 14 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kemal Polat | 1 | 1348 | 97.38 |
| Sadik Kara | 2 | 275 | 27.39 |
| Ayşegül Güven | 3 | 86 | 9.08 |
| Salih Güneş | 4 | 1267 | 78.53 |
| KaraSadık | 5 | 36 | 3.47 |
| GüvenAyşegül | 6 | 11 | 0.98 |