Intelligent regression techniques for non-exercise prediction of VO2max

The purpose of this study is to develop non-exercise (N-Ex) VO₂max prediction models by using Support Vector Regression (SVR) and Multilayer Feed Forward Neural Networks (MFFNN). VO₂max values of 100 subjects 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.