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Title: Multiclass support vector machines for diagnosis of erythemato-squamous diseases
Authors: Übeyli, Elif Derya
Keywords: multiclass support vector machine (SVM)
error correcting output codes (ECOC)
recurrent neural network (RNN)
erythemato-squamous diseases
Issue Date: 2008
Publisher: Pergamon-Elsevier Science Ltd
Abstract: A new approach based oil the implementation of multiclass support vector machine (SVM) with the error correcting output codes (ECOC) is presented for diagnosis of erythemato-squamous diseases. The recurrent neural network (RNN) and multilayer perceptron neural network (MLPNN) were also tested and benchmarked for their performance on the diagnosis of the erythemato-squamous diseases. The domain contained records of patients with known diagnosis. Given a training set of such records, the classifiers learned how to differentiate a new case in the domain. The classifiers were used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. The purpose is to determine all optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and the multiclass SVM and RNN trained oil these features achieved high classification accuracies. (C) 2007 Elsevier Ltd. All rights reserved.
ISSN: 0957-4174
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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