Non-exercise-based racing time prediction of cross-country skiers using machine learning methods combined with Relief-F feature selection

Abstract

This study proposes new non-exercise models for estimating the racing time of cross-country skiers. Machine learning methods employed to build the prediction models include General Regression Neural Network (GRNN), Support Vector Machine (SVM), Multilayer Feed-Forward Artificial Neural Network (MFANN), and Radial Basis Function Neural Network (RBFNN), whereas the Relief-F algorithm combined with a ranker search has been utilized as the feature selector. The self-created data set contains samples collected from 370 cross-country skiers with inhomogeneous capabilities. Each sample in the data set contains physiological variables such as sex, age, height, weight, and body mass index (BMI) combined with an immersive set of survey data. The outcomes suggest that generally, the GRNN-based models exhibit the best prediction performance and can be used as a feasible tool for the prediction of the racing time of cross-country skiers with tolerable root mean square errors (RMSEs). It is seen that inclusion of age and assigned starting wave of cross-country skiers in models leads to much lower RMSEs, suggesting that the racing time of cross-country skiers is highly correlated to these two predictor variables. When compared with the exercise-based models, the proposed non-exercise-based models produce consistently comparable prediction performance for all evaluated machine learning methods. The non-exercise-based models have the relevant benefit of practical feasibility, as the models do not require the skiers to complete physical exercises and are also applicable to a wide range of cross-country skiers.