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Quadriceps refers to a group of four muscles on the front of the thigh. Adequate quadriceps strength is essential for athletic performance. Quadriceps strength in athletes can be reliably assessed using isokinetic dynamometry. Also, some studies in literature showed the possibility of predicting the quadriceps strength of athletes using machine learning methods within acceptable error rates. The purpose of this study is to investigate the effect of sport branch on quadriceps strength prediction using Support Vector Machine (SVM). The dataset included 70 athletes selected from the College of Physical Education and Sport at Gazi University. The optimal values of SVM parameters have been found by using grid search. The predictor variables gender, age, height, weight and sport branch have been utilized to build sixteen different quadriceps strength prediction models. By carrying out 10-fold cross-validation, the performance of the prediction models has been evaluated by calculating the root mean square errors (RMSE's) and multiple correlation coefficients (R's). The results show that the RMSE's of the prediction models change from 23.31 to 47.78 Nm. The model including the predictor variables gender, height, weight and sport branch yields the lowest RMSE and highest R. One can conclude that sport branch has a profound effect for predicting the quadriceps strength of athletes.
Keywords: Support vector machines, quadriceps, prediction.
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