Prediction of Hamstring and Quadriceps muscle strength of athletes using machine learning methods

The success of athletes is closely related to the muscles of the thigh, namely the hamstring and quadriceps. Among the different techniques used for measuring the hamstring and quadriceps muscle strength, the use of isokinetic equipments is the most accurate. However, their utilization is associated with several difficulties and limitations. The aim of this study is to build new and more comprehensive models for predicting the hamstring and quadriceps muscle strength of athletes using four main machine learning methods, namely Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), Radial Basis Function Neural Network (RBFNN) and Single Decision Tree (SDT). The root mean square errors (RMSE's) and the multiple correlation coefficients (R's) have been used for computing the prediction errors. On the basis of the results obtained, it has been proved that machine learning methods especially SVM can be used for the hamstring and quadriceps muscle strength prediction with an acceptable accuracy