Prediction of Vo2max With Submaximal and Questionnaire Variables: Using different regression methods - Couverture souple

Yücel, Eser; Akay, M. Fatih

 
9783659806155: Prediction of Vo2max With Submaximal and Questionnaire Variables: Using different regression methods
Couverture souple
ISBN 10 : 3659806153 ISBN 13 : 9783659806155
Editeur : LAP LAMBERT Academic Publishing, 2015

Synopsis

Maximal oxygen uptake (VO2max) refers to the maximum amount of oxygen that an individual can utilize during intense or maximal exercise. The purpose of this thesis is to develop accurate VO2max prediction models using submaximal and questionnaire variables. Regression methods such as Support Vector Machines (SVM), Multilayer Perceptron (MLP) and Multiple Linear Regression (MLR) have been used for developing VO2max prediction models. The performance of prediction models has been evaluated by calculating their multiple correlation coefficients (R’s) and standard error of estimates (SEE’s). The results show that the accuracy of VO2max prediction models based on submaximal and standard non-exercise variables could be significantly improved by including questionnaire variables in prediction models. The results of SVM models have been also compared with the ones obtained by MLP and MLR and it turned out that SVM-based VO2max prediction models perform better (i.e. yield lower SEE’s and higher R’s) than the prediction models developed by other regression methods.

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Présentation de l'éditeur

Maximal oxygen uptake (VO2max) refers to the maximum amount of oxygen that an individual can utilize during intense or maximal exercise. The purpose of this thesis is to develop accurate VO2max prediction models using submaximal and questionnaire variables. Regression methods such as Support Vector Machines (SVM), Multilayer Perceptron (MLP) and Multiple Linear Regression (MLR) have been used for developing VO2max prediction models. The performance of prediction models has been evaluated by calculating their multiple correlation coefficients (R’s) and standard error of estimates (SEE’s). The results show that the accuracy of VO2max prediction models based on submaximal and standard non-exercise variables could be significantly improved by including questionnaire variables in prediction models. The results of SVM models have been also compared with the ones obtained by MLP and MLR and it turned out that SVM-based VO2max prediction models perform better (i.e. yield lower SEE’s and higher R’s) than the prediction models developed by other regression methods.

Biographie de l'auteur

Eser Yücel graduated from Mersin University, Computer Engineering. He studied his MSc degree in Cukurova University. He has worked as a system administrator at several companies in Turkey. He is married and has a son (Çınar)

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2015
Langue
anglais
ISBN 10 
3659806153
ISBN 13 
9783659806155
Reliure
Broché
Nombre de pages
96
Coordonnées du fabricant
non disponible
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