Abstract 13030: Non-Exercise Machine Learning Model for Maximal Oxygen Uptake Prediction in National Population Surveys

Abstract

Introduction: Maximal oxygen uptake (VO 2 max), an indicator of cardiorespiratory fitness (CRF) requires exercise testing and, as a result, is rarely ascertained in large-scale population-based studies. There are many non-exercise algorithms that can estimate VO 2 max, but they are limited by their non-representative study population, lack of predictors, and the insufficient predictive power for the type of model chosen. In this study, we aim to improve the non-exercise algorithms using machine learning (ML) methods and data from U.S. national population surveys. Methods: We used the 1999-2004 data from the National Health and Nutrition Examination Survey (NHANES) because it included exercise testing to measure VO 2 max. Based on the literature review, predictors were identified from demographic, interview, examination, and laboratory data. The study population was split into a training set (80%) and a testing set (20%) for model development and validation. We built the improved model using Light Gradient Boosting Machine (LightGBM) for its outstanding performance and built-in functionality for handling missingness. The model was trained by optimizing Root-Mean Squared Error (RMSE) and using 5-fold cross-validation. Existing non-exercise algorithms for comparison were applied to the testing set. Results: Among the 5,668 participants included, the mean age was 32.5 and 49.9% were women. 40 predictors in total were selected for the final feature set. The fitted LightGBM model achieved an RMSE of 8.43 ml/kg/min (95% CI: 7.64 -9.19) on the testing set, significantly reducing the error by 14% (P <0.05) compared with the best existing non-exercise algorithms that could be cross-validated in NHANES (Table1). Conclusion: In conclusion, the extended non-exercise ML model in this study can provide a more accurate prediction of VO 2 max for NHANES participants, and those with similar data, than the existing non-exercise algorithms.