Comparison of neural network and Bayes recognition approaches to the diagnostic of multiple sclerosis

Youval Nehmadi, Hugo Guterman

Research output: Contribution to conferencePaperpeer-review

Abstract

This article describes the application of Multi-Layer Perceptron (MLP) to the problem of diagnosing Multiple Sclerosis (MS). The classification information is obtained by Trigeminal Evoked Potential (TEP) test. The performance of the MLP is compared with that of the human experts and the Bayes classifier. The efficiency of the neural network and the classical classifiers in conjunction with 4 types of features: the Fourier transform (FT), the peak position, the ARX model coefficient and the temporal wave form, are examined. Although a large clinical data base would be necessary, before this approach can be fully validated, the initial results are very promising. The MLP was found to be less susceptible to the number of features used. The ability of the MLP classifier to generalize is far better than that of the Bayes classifier.

Original languageEnglish
Pages1.2.3/1-5
StatePublished - 1 Jan 1995
EventProceedings of the 18th Convention of Electrical and Electronics Engineers in Israel - Tel Aviv, Isr
Duration: 7 Mar 19958 Mar 1995

Conference

ConferenceProceedings of the 18th Convention of Electrical and Electronics Engineers in Israel
CityTel Aviv, Isr
Period7/03/958/03/95

ASJC Scopus subject areas

  • Engineering (all)

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