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Title: Parkinson's disease recognition using gauss map based chaotic particle swarm-neural network
Authors: Koyuncu, Hasan
Keywords: Chaotic
Gauss map
Hybrid classifiers
Parkinson's disease
Pattern recognition
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: In detection of Parkinson's disease (PD), voice recordings are frequently appealed to reveal whether disease is available or not. The features extracted from these recordings are utilized as the input of classification methods. Herein, binary classification of features gains importance to accurately perform the PD detection. In this paper, we perform the classification of two well-known PD datasets including the features attained by recordings. Efficient hybrid classifiers are formed using the state-of-the-art optimization algorithms originated from particle swarm optimization (PSO). As an efficient classifier, neural network (NN) is determined as the main part of hybrid architecture. Sine map based chaotic PSO (SM-CPSO), dynamic weight PSO (DWPSO) and chaotic dynamic weight PSO (CDW-PSO) are considered to compare with Gauss map based CPSO (GMCPSO) on formation of hybrid classifiers and on classification of PD. For a detailed assessment, seven metrics (accuracy, AUC, sensitivity, specificity, g-mean, precision, f-measure) based comparison is realized, and 2-fold cross validation is handled to test the system. According to the results, GM-CPSO-NN achieves to remarkable performance among other hybrid methods and also outperforms to the recent literature studies. Consequently, a comprehensive study about PD recognition is realized, and a detailed comparison of hybrid NNs is presented on pattern classification. © 2019 IEEE.
Description: 2019 International Conference on Engineering and Telecommunication, EnT 2019 -- 20 November 2019 through 21 November 2019 -- -- 158475
ISBN: 9781728135649
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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