Predicton of maximal oxygen uptake using machine learning methods combined with feature selection

Maximal oxygen uptake (VO2max) refers to the maximum amount of oxygen that an individual can utilize during intense or maximal exercise. Although numerous studies exist to predict VO2max, to date, no study has attempted to apply machine learning methods combined with feature selection algorithms to identify the discriminative features for prediction of VO2max. The purpose of this thesis is to develop new VO2max prediction models using Support Vector Machines (SVM) and Multilayer Perceptron (MLP) combined with feature selection algorithms. Two feature selection algorithms, Relief-F and Correlation-based Feature Selection (CFS), have been considered. By applying Relief-F on the full data sets, the ranking of the features have been obtained. Dimensionality of data sets is reduced by removing the feature with the lowest score at a time before being passed on to the regression method. CFS has been used separately on the full data sets to find out the best subset of features. Using 10-fold cross validation on four different data sets, the performance of prediction models has been evaluated by calculating their multiple correlation coefficients (R's) and standart error of estimates (SEE's). The results show that SVM-based VO2max prediction models perform better (i.e. yield lower SEE's and higher R's) than the prediction models developed by other regression methods.