Yayın: A comparison of regression methods for remote tracking of Parkinson's disease progression
Tarih
Kurum Yazarları
Eskidere, Ömer
Ertaş, Figen
Hanilci, Cemal
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Yayıncı:
Pergamon-Elsevier Science
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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.
Açıklama
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Konusu
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
Alıntı
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.