Confirmatory Study on Prediction of Maximal Oxygen Uptake by Ensemble Learning Algorithm

Abstract

Objective: The prediction model of maximal oxygen uptake (VO₂max) was established by different ensemble learning methods. Compared with multiple linear regression, a common prediction method of VO₂max, the effectiveness of the ensemble learning model is verified, and screen out the optimal VO₂max prediction model. Methods: A total of 84 male college students, were recruited, informed of the purpose and procedure of the experiment, and passed the PAR-Q questionnaire screening. Cardiopulmonary Exercise Test (CPET), lung function, body composition test, VO₂max, minute ventilation volume (VE), tidal volume (Vt), weight, and other related indicators. After the correlation analysis of the experimental results, a VO₂max multiple linear regression model and an ensemble learning prediction model are computed. Results: The indicators that finally enter the multiple linear regression equation include: weight, Vt, VE. The regression equation is: VO₂max (ml/kg/min) = 52.455+0.005×VT (ml/min) + 0.111×VE (L/min)-0.441×weight (kg), and the regression coefficient test results is extremely significant (P<0.01). The VO₂max prediction model is computed by using the ensemble learning algorithm and among the five prediction models, it is found that Weighted average (R^2=0.7715) has the best effect, and higher than multiple regression model, there is no significant difference between the actual value and the predicted value VO₂max (P>0.05). Conclusion: In the case of a small sample size, the weighted average model predicts VO₂max the most accurately, compared with multiple regression models, the ensemble learning model can include more basic indicators, the evaluation of VO₂max in the body is more comprehensive, and the prediction results are more accurate.