Main Article Content
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.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).