Utilization of Discretization method on the diagnosis of optic nerve disease

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dc.contributor.authorPolat, Kemal
dc.contributor.authorKara, Sadik
dc.contributor.authorGueven, Ayseguel
dc.contributor.authorGuenes, Salih
dc.date.accessioned2020-03-26T17:28:25Z
dc.date.available2020-03-26T17:28:25Z
dc.date.issued2008
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe 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. (C) 2008 Elsevier Ireland Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2008.04.009en_US
dc.identifier.endpage264en_US
dc.identifier.issn0169-2607en_US
dc.identifier.issn1872-7565en_US
dc.identifier.issue3en_US
dc.identifier.pmid18571280en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage255en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.cmpb.2008.04.009
dc.identifier.urihttps://hdl.handle.net/20.500.12395/22777
dc.identifier.volume91en_US
dc.identifier.wosWOS:000258258400008en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherELSEVIER IRELAND LTDen_US
dc.relation.ispartofCOMPUTER METHODS AND PROGRAMS IN BIOMEDICINEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectoptic nerve diseaseen_US
dc.subjectC4.5 decision tree classifieren_US
dc.subjectartificial neural networken_US
dc.subjectleast square support vector machineen_US
dc.subjectdiscretization methoden_US
dc.subjectVEP signalsen_US
dc.subjecthybrid systemsen_US
dc.titleUtilization of Discretization method on the diagnosis of optic nerve diseaseen_US
dc.typeArticleen_US

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