Prediction of maximum muscular endurance time involving four stabilization exercises assessments using machine learning methods

The muscular endurance time is viewed an important component influencing the performance of athletes in various sport branches, such as cycling, rowing, cross-country skiing, swimming and running. Due to several drawbacks of direct measurement, researchers need alternative ways to determine maximum muscular endurance time. The aim of this thesis is to build new models for predicting the maximum endurance time using demographic variables (age, gender, height, weight and body mass index), submaximal data (rating of perceived exertion) and machine learning methods including support vector machines (SVM), multilayer perceptron (MLP), generalized regression neural network (GRNN), radial basis function (RBF) and single decision tree (SDT). The root mean square error (RMSE) and multiple correlation coefficient (R) have been used for assesing the performance of prediction models. The results suggest that SVM is a viable method for maximum endurance time prediction with an acceptable accuracy.