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

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.