An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine release_e7jy6xysrjckbiuk5antromrv4

by Derya Avci, Akif Dogantekin

Published in Parkinson's Disease by Hindawi Limited.

2016   Volume 2016, p1-9

Abstract

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.
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Type  article-journal
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Year   2016
Language   en ?
DOI  10.1155/2016/5264743
PubMed  27274882
PMC  PMC4871978
Wikidata  Q30382071
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