2021-12-022021-12-022012-04Eskidere, Ö. vd. (2012). "A comparison of regression methods for remote tracking of Parkinson's disease progression". Expert Systems with Applications, 39(5), 5523-5528.0957-41741873-6793https://doi.org/10.1016/j.eswa.2011.11.067https://www.sciencedirect.com/science/article/pii/S0957417411016137http://hdl.handle.net/11452/22938Remote 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.eninfo:eu-repo/semantics/closedAccessComputer scienceEngineeringOperations research & management scienceParkinson's diseaseUnified parkinson's disease rating scaleLeast square support vector machine regressionNeural-networksRatingsVoiceLeast squares approximationsNeural networksNeurodegenerative diseasesRegression analysisGeneral regression neural networkLeast square support vector machinesLower costMultilayer perceptron neural networksNon-invasivePatient trackingRegressionRegression methodRemote trackingSupport vector machinesArticle0003011553000892-s2.0-8485588606055235528395Computer science, artificial intelligenceEngineering, electrical & electronicOperations research & management scienceParkinson's Disease; Voice Disorders; Speech Signal