# Machine Learning Regressors to Estimate Continuous Oxygen Uptakees (\(\dot{VO}_{2}\))
## 4 Discussion  
In our study, we initially examined the baseline characteristics of participants, revealing a cohort of 532 unique individuals, predominantly male at 87.76%, with females comprising 12.24%. Analysis of the age distribution showed similar patterns across genders, with males averaging 32.72 years (\(\pm\)8.86 SD) and females 29.94 years (\(\pm\)7.44 SD). Disparities in height and weight were evident, with males exhibiting higher averages of 176.62 cm (\(\pm\)6.53 SD) and 76.40 kg (\(\pm\)10.07 SD) compared to females at 165.88 cm (\(\pm\)7.55 SD) and 60.81 kg (\(\pm\)10.02 SD), respectively. Moreover, \(\dot{V}O_{2 max}\) and min values showcased significant gender differences, with males displaying higher values: \(\dot{V}O_{2 max}\) at 2421.38 mL/min (\(\pm\)985.61 SD) and \(\dot{V}O_{2}\) min at 31.94 mL/kg/min (\(\pm\)13.05 SD), compared to females at 1670.74 mL/min (\(\pm\)693.45 SD) and 27.59 mL/kg/min (\(\pm\)10.85 SD), respectively. Moving on to the continuous estimation of \(\dot{V}O_{2}\), various regression models were evaluated, among which XGBoost demonstrated superior performance, with an MAE of 0.1834, MSE of 0.0640, RMSE of 0.2529, and a significant R-squared value of 0.9996. Fine-tuning of the XGBoost model further improved its performance, achieving a mean MAE of 0.1217 (\(\pm\)0.0020) and a mean MAPE of 0.0056 (\(\pm\)0.0001) on validation. The model maintained its efficacy on an independent test dataset, retaining an MAE of 0.1793, MSE of 0.1460, and an R-squared value of 0.9991. These results underscore the robustness of the XGBoost model in accurately predicting \(\dot{V}O_{2}\) levels. Furthermore, the estimation of \(\dot{V}O_{2 max}\) yielded promising results, with a high correlation reflected in an \(R^{2}\) value of 0.9691. The model exhibited an MAE of 0.7109, MSE of 2.5859, RMSE of 1.6081, RMSLE of 0.0277, and MAPE of 1.2971, indicating its effectiveness in predicting \(\dot{V}O_{2 max}\). Overall, these findings emphasize the model's potential utility in estimating physiological parameters, with implications for health monitoring and fitness assessment. Our study also provides an insightful comparison between the machine learning-based approach and traditional \(\dot{V}O_{2 max}\) estimation equations, such as those presented by [PERSON] et al., [PERSON] et al., [PERSON] et al., and [PERSON] (2016); [PERSON] et al. (2017); [PERSON] et al. (2018); [PERSON] et al. (2018) (Table 1). Unlike the conventional equations, which rely heavily on demographic factors, like age, weight, and height, our machine learning models incorporate real-time data, allowing for a more accurate and individualized prediction of \(\dot{V}O_{2 max}\). The improvements in the MAE, RMSE, and other performance metrics highlight the enhanced predictive power of AI algorithms, particularly when fine-tuned through methods like hyperparameter optimization in XGBoost. These advancements are critical for applications requiring continuous monitoring and real-time feedback, which are incomprehensible with static equations. The Spearman correlation coefficient matrix provides insights into \(\dot{V}O_{2}\) and its relationship with other parameters. Although we followed the equations to select parameters, height and weight showed a relatively high correlation, along with a smaller positive correlation with age. This suggests that taller and heavier individuals tend to have higher \(\dot{V}O_{2 max}\) values. The scatter plot displaying the relationship between actual \(\dot{V}O_{2 max}\) (mL/kg/min) and its estimation demonstrates strong predictive accuracy, as indicated by a high R-squared value of 0.9691. This strong linear relationship confirms the reliability of the continuous \(\dot{V}O_{2}\) estimation methods used in this study. Compared to the study from [PERSON], [PERSON] et al. (2015), our study differs in model selection, input variables, and target population.