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

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.