Prediction of Maximum Oxygen Uptake with Different Machine Learning Methods by Using Submaximal Data

Maximum oxygen uptake (VO(2)max) 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 VO(2)max 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.