Predicting VO2max from submaximal exercise and non-exercise data using artificial neural networks [Yapay Sinir aglari kullanilarak submaksimal egzersiz ve egzersize dayali olmayan verilerden VO2max tahmini]

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dc.contributor.authorAkay, Mehmet Fatih
dc.contributor.authorAktürk, E.
dc.contributor.authorTunçdemir, Ali Erçe
dc.contributor.authorŞen, Nezih Nusret
dc.date.accessioned12.07.201910:50:10
dc.date.accessioned2019-07-12T15:25:46Z
dc.date.available12.07.201910:50:10
dc.date.available2019-07-12T15:25:46Z
dc.date.issued2013
dc.departmentFen Edebiyat Fakültesien_US
dc.description2013 21st Signal Processing and Communications Applications Conference, SIU 2013 -- 24 April 2013 through 26 April 2013 -- Haspolaten_US
dc.description.abstractThe purpose of this study is to develop new multilayer feed-forward artificial neural network (ANN)-based maximal oxygen uptake (VO2max) prediction models by using submaximal treadmill exercise and nonexercise data. Using 10- fold cross validation on the dataset, standard error of estimate (SEE) and multiple correlation coefficient (R) of the models are calculated. It is shown that the models including submaximal, standard nonexercise and questionnaire variables yield higher R and lower SEE than the ones including submaximal and standard nonexercise variables only. The results of ANN-based models are also compared with the ones obtained by Multiple Linear Regression (MLR) and Support Vector Machines (SVM). It is shown that ANN-based models perform better than MLR and SVM-based models for predicting VO2max.en_US
dc.identifier.doi10.1109/SIU.2013.6531406
dc.identifier.isbn9781467355629
dc.identifier.scopus2-s2.0-84880900572
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/SIU.2013.6531406
dc.identifier.urihttps://hdl.handle.net/20.500.12507/428
dc.indekslendigikaynakScopus
dc.language.isotr
dc.relation.ispartof2013 21st Signal Processing and Communications Applications Conference, SIU 2013
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectCardiorespiratory Fitnessen_US
dc.subjectSubmaximal Exercise Testen_US
dc.subjectVO2maxen_US
dc.titlePredicting VO2max from submaximal exercise and non-exercise data using artificial neural networks [Yapay Sinir aglari kullanilarak submaksimal egzersiz ve egzersize dayali olmayan verilerden VO2max tahmini]
dc.typeConference Object

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