Machine Learning Regressors to Estimate Continuous Oxygen Uptakes (VO2)

Abstract

Oxygen consumption ((VO2)-O-center dot) estimation is vital for evaluating aerobic performance and cardiovascular fitness. This study explores various regression models to develop a real-time (VO2)-O-center dot and (VO2max)-O-center dot estimation model. Utilizing a dataset from PhysioNet, encompassing cardiorespiratory measurements from 992 treadmill tests conducted at the University of Malaga's Exercise Physiology and Human Performance Lab from 2008 to 2018, participants aged 10 to 63, including amateur and professional athletes, underwent breath-by-breath monitoring of physiological parameters. The study underlines the efficacy of regressor models in handling complex datasets and developing a robust real-time (VO2)-O-center dot estimation model. After adjusting parameters to (VO2)-O-center dot in "mL/kg/min" from "mL/min", and selecting 'Age', 'Weight', 'Height', 'HR', 'Sex', and 'Time' as parameters for (VO2)-O-center dot estimation, XGBoost emerged as the optimal choice. Validation using a test dataset of 132 participants yielded the following results for Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R-2), Root Mean Squared Logarithmic Error (RMSLE), and Mean Absolute Percentage Error (MAPE) metrics: MAE of 0.1793, MSE of 0.1460, RMSE of 0.3821, R-2 of 0.9991, RMSLE of 0.0140, and MAPE of 0.0066. This study demonstrates the effectiveness of various regressor models in developing a continuous (VO2max)-O-center dot estimation model that has promising performance metrics.