Support vector regression and multilayer feed forward neural networks for non-exercise prediction of VO2max

Authors:
Mehmet Fatih Akay;Cigdem Inan;Danielle I. Bradshaw;James D. George
Affiliations:
Cukurova University, Dept. of Computer Engineering, Adana 01330, Turkey;Cukurova University, Dept. of Computer Engineering, Adana 01330, Turkey;Huntsman Cancer Hospital, 1950 Circle of Hope, Salt Lake City, UT 84112-5550, USA;Brigham Young University, Dept. of Exercise Sciences, Provo, UT 84602, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

The purpose of this study is to develop non-exercise (N-Ex) VO"2max prediction models by using support vector regression (SVR) and multilayer feed forward neural networks (MFFNN). VO"2max 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 (3.23mlkg^-^1min^-^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 VO"2max for heterogeneous samples.