A comparison of regression methods for remote tracking of Parkinson's disease progression

Authors:
Ömer Eskidere;Figen Ertaş;Cemal Hanilçi
Affiliations:
Uludag University, Vocational School of Technical Sciences, 16059 Bursa, Turkey;Uludag University, Electronic Engineering Department, 16059 Bursa, Turkey;Uludag University, Electronic Engineering Department, 16059 Bursa, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

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.