Estimating the maximal oxygen uptake with new prediction models for college-aged students using feature selection algorithm

Abstract

Maximum oxygen consumption (VO2max) is important to observe the endurance of the athletes and evaluate their performance.. Aim is to develop new prediction models for college-aged students using Support Vector Machine (SVM) with Relief-F feature selection algorithm. Ten different models consisting of the predictor variables gender, age, weight, height, maximal heart rate (HRmax), time, speed, Perceived Functional Ability scores (PFA-1 and PFA-2) and Physical Activity Rating score (PA-R) have been created by Relief-F scores for prediction of VO2max. The prediction models’ standard error of estimates (SEE’s) and multiple correlation coefficients (R’s) have been calculated for evaluating their performances. For comparison purposes, Tree Boost (TB) and Radial Basis Function Network (RBFN) based models have also been developed. The results show that the prediction model including PAR, speed, time, weight, PFA-1, gender and HRmax gives the lowest SEE with 6.42 mL.kg−1.min−1 and highest R with 0.79. Also, this study shows that the predictor variables HRmax and gender play a considerable role in VO2max prediction.

Keywords: Maximum oxygen uptake, machine learning, feature selection.