Bu öğeden alıntı yapmak, öğeye bağlanmak için bu tanımlayıcıyı kullanınız: http://hdl.handle.net/11452/22938
Başlık: A comparison of regression methods for remote tracking of Parkinson's disease progression
Yazarlar: Uludağ Üniversitesi/Teknik Bilimler Meslek Yüksekokulu.
Uludağ Üniversitesi/Mühendislik Fakültesi/Elektronik Mühendisliği Bölümü.
Eskidere, Ömer
Ertaş, Figen
Hanilci, Cemal
AAH-4188-2021
S-4967-2016
24723995200
24724154500
35781455400
Anahtar kelimeler: Computer science
Engineering
Operations research & management science
Parkinson's disease
Unified parkinson's disease rating scale
Least square support vector machine regression
Neural-networks
Ratings
Voice
Least squares approximations
Neural networks
Neurodegenerative diseases
Regression analysis
General regression neural network
Least square support vector machines
Lower cost
Multilayer perceptron neural networks
Non-invasive
Patient tracking
Regression
Regression method
Remote tracking
Support vector machines
Yayın Tarihi: Nis-2012
Yayıncı: Pergamon-Elsevier Science
Atıf: Eskidere, Ö. vd. (2012). "A comparison of regression methods for remote tracking of Parkinson's disease progression". Expert Systems with Applications, 39(5), 5523-5528.
Özet: Remote patient tracking has recently gained increased attention, due to its lower cost and non-invasive nature. In this paper, the performance of Support Vector Machines (SVM), Least Square Support Vector Machines (LS-SVM), Multilayer Perceptron Neural Network (MLPNN), and General Regression Neural Network (GRNN) regression methods is studied in application to remote tracking of Parkinson's disease progression. Results indicate that the LS-SVM provides the best performance among the other three, and its performance is superior to that of the latest proposed regression method published in the literature.
URI: https://doi.org/10.1016/j.eswa.2011.11.067
https://www.sciencedirect.com/science/article/pii/S0957417411016137
http://hdl.handle.net/11452/22938
ISSN: 0957-4174
1873-6793
Koleksiyonlarda Görünür:Scopus
Web of Science

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