Performance comparison of different regression methods for maximal oxygen uptake estimation of cross-country skiers

Maximal oxygen uptake (VO₂max) is the one of the most important determinants of cross-country ski race performance. The purpose of this study is to develop new VO₂max prediction models for cross-country skiers by using General Regression Neural Network (GRNN), Cascade Correlation Network (CCN) and Single Decision Tree (SDT). In order to develop VO₂max prediction models, a dataset including data of 139 subjects and the input variables age, gender, height, weight, body mass index (BMI), heart rate at lactate threshold (HRLT), maximum heart rate (HRmax) and time have been used. Applying 10-fold cross validation on the dataset, multiple correlation coefficients (R´s) and standard error of estimates (SEE´s) of the models have been calculated. It is shown that GRNN-based models yield 12.13% and 25.50% lower SEE´s on the average than the ones obtained by CCN-based and SDT- based models.