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

Simple item page
dc.contributor.buuauthorEskidere, Ömer
dc.contributor.buuauthorErtaş, Figen
dc.contributor.buuauthorHanilci, Cemal
dc.contributor.departmentUludağ Üniversitesi/Teknik Bilimler Meslek Yüksekokulu.tr_TR
dc.contributor.departmentUludağ Üniversitesi/Mühendislik Fakültesi/Elektronik Mühendisliği Bölümü.tr_TR
dc.contributor.researcheridAAH-4188-2021tr_TR
dc.contributor.researcheridS-4967-2016tr_TR
dc.contributor.scopusid24723995200tr_TR
dc.contributor.scopusid24724154500tr_TR
dc.contributor.scopusid35781455400tr_TR
dc.date.accessioned2021-12-02T05:51:19Z
dc.date.available2021-12-02T05:51:19Z
dc.date.issued2012-04
dc.description.abstractRemote 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.en_US
dc.identifier.citationEskidere, Ö. vd. (2012). "A comparison of regression methods for remote tracking of Parkinson's disease progression". Expert Systems with Applications, 39(5), 5523-5528.en_US
dc.identifier.endpage5528tr_TR
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue5tr_TR
dc.identifier.scopus2-s2.0-84855886060tr_TR
dc.identifier.startpage5523tr_TR
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2011.11.067
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0957417411016137
dc.identifier.urihttp://hdl.handle.net/11452/22938
dc.identifier.volume39tr_TR
dc.identifier.wos000301155300089tr_TR
dc.indexed.scopusScopusen_US
dc.indexed.wosSCIEen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Scienceen_US
dc.relation.journalExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputer scienceen_US
dc.subjectEngineeringen_US
dc.subjectOperations research & management scienceen_US
dc.subjectParkinson's diseaseen_US
dc.subjectUnified parkinson's disease rating scaleen_US
dc.subjectLeast square support vector machine regressionen_US
dc.subjectNeural-networksen_US
dc.subjectRatingsen_US
dc.subjectVoiceen_US
dc.subjectLeast squares approximationsen_US
dc.subjectNeural networksen_US
dc.subjectNeurodegenerative diseasesen_US
dc.subjectRegression analysisen_US
dc.subjectGeneral regression neural networken_US
dc.subjectLeast square support vector machinesen_US
dc.subjectLower costen_US
dc.subjectMultilayer perceptron neural networksen_US
dc.subjectNon-invasiveen_US
dc.subjectPatient trackingen_US
dc.subjectRegressionen_US
dc.subjectRegression methoden_US
dc.subjectRemote trackingen_US
dc.subjectSupport vector machinesen_US
dc.subject.scopusParkinson's Disease; Voice Disorders; Speech Signalen_US
dc.subject.wosComputer science, artificial intelligenceen_US
dc.subject.wosEngineering, electrical & electronicen_US
dc.subject.wosOperations research & management scienceen_US
dc.titleA comparison of regression methods for remote tracking of Parkinson's disease progressionen_US
dc.typeArticle
dc.wos.quartileQ2 (Computer science, artificial intelligence)en_US
dc.wos.quartileQ1en_US

Files

Collections

Web of Science
Scopus