Predicting VO2max from submaximal exercise and non-exercise data using artificial neural networks [Yapay Sinir aglari kullanilarak submaksimal egzersiz ve egzersize dayali olmayan verilerden VO2max tahmini]

Abstract

The purpose of this study is to develop new multilayer feed-forward artificial neural network (ANN)-based maximal oxygen uptake (VO2max) prediction models by using submaximal treadmill exercise and nonexercise data. Using 10- fold cross validation on the dataset, standard error of estimate (SEE) and multiple correlation coefficient (R) of the models are calculated. It is shown that the models including submaximal, standard nonexercise and questionnaire variables yield higher R and lower SEE than the ones including submaximal and standard nonexercise variables only. The results of ANN-based models are also compared with the ones obtained by Multiple Linear Regression (MLR) and Support Vector Machines (SVM). It is shown that ANN-based models perform better than MLR and SVM-based models for predicting VO2max.