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The purpose of this work is to develop VO2max prediction models by using non-exercise, submaximal and hybrid variables by using Support Vector Machines (SVM), Multi-layer Feed-forward Artificial Neural Networks (MFANN) and Multiple Linear Regression (MLR) on different data sets. Using 10-fold cross validation on four different data sets, the performance of prediction models has been evaluated by calculating their multiple correlation coefficients (Rs) and standard error of estimates (SEEs). The results show that SVM-based VO2max prediction models perform better (i.e. yield lower SEEs and...