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


Eskidere O., ERTAŞ F., Hanilci C.

EXPERT SYSTEMS WITH APPLICATIONS, vol.39, no.5, pp.5523-5528, 2012 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 39 Issue: 5
  • Publication Date: 2012
  • Doi Number: 10.1016/j.eswa.2011.11.067
  • Journal Name: EXPERT SYSTEMS WITH APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.5523-5528

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. (C) 2011 Elsevier Ltd. All rights reserved.