Support vector regression and multilayer feed forward neural networks for non-exercise prediction of VO2 max
The purpose of this study is to develop non-exercise (N-Ex) VO2 max prediction models by using support vector regression (SVR) and multilayer feed forward neural networks (MFFNN). VO2 max values of 100 subjects (50 males and 50 females) are measured using a maximal graded exercise test. The variables: gender, age, body mass index (BMI), perceived functional ability (PFA) to walk, jog or run given distances and current physical activity rating (PA-R) are used to build two N-Ex prediction models. Using 10-fold cross validation on the dataset, standard error of estimates (SEE) and multiple correlation coefficients (R) of both models are calculated. The MFFNN-based model yields lower SEE (3.23 ml kg(-1) min(-1)) whereas the SVR-based model yields higher R (0.93). Compared with the results of the other N-Ex prediction models in literature that are developed using multiple linear regression analysis, the reported values of SEE and R in this study are considerably more accurate. Therefore, the results Suggest that SVR-based and MFFNN-based N-Ex prediction models can be valid predictors of VO2 max for heterogeneous samples. (C) 2009 Elsevier Ltd. All rights reserved.
support vector regression multilayer feed forward neural networks vo2 max cardiorespiratory fitness