Prediction of maximum oxygen uptake with different machine learning methods by using submaximal data [Submaksimal Veriler Kullanilarak Farki Makine Ögrenme Yöntemleri ile Maksimum Oksijen Tüketiminin Tahmin Edilmesi]

2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 --16 May 2015 through 19 May 2015 -- --Maximum oxygen uptake (VO2max) is the highest amount of oxygen used by the body during intense exercise and is an important component to determine cardiorespiratory fitness. In this study, models have been developed for predicting VO2max with four different machine learning methods. These methods are Treeboost (TB), Decision Tree Forest (DTF), Gene Expression Programming (GEP) and Single Decision Tree (SDT). The predictor variables used to develop prediction models include gender, age, weight, height, treadmill speed, heart rate and stage. The performance of the prediction models have been evaluated by calculating Standard Error of Estimate (SEE) and Multiple Correlation Coefficient (R) and using 10-fold cross validation. Results show that compared to the SEE's of TB, the maximum percentage decrement rates in SEE's of DTF, GEP and SDT are 8.38%, 12.97% and 23.07%, respectively. © 2015 IEEE