Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
384889 | Expert Systems with Applications | 2012 | 6 Pages |
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.
► Performance of LS-SVM, SVM, MLPNN, and GRNN are compared in remote tracking of Parkinson Disease progression. ► LS-SVM outperforms the other three in mapping vocal features to UPDRS data. ► Log transformation provides better tracking performance with most data sets. ► Motor-UPDRS and total-UPDRS results are 1.08 and 1.34 point better than existing results.