用于全国人群调查中最大摄氧量预测的非运动机器学习模型。
Nonexercise machine learning models for maximal oxygen uptake prediction in national population surveys.
机构信息
Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA.
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
出版信息
J Am Med Inform Assoc. 2023 Apr 19;30(5):943-952. doi: 10.1093/jamia/ocad035.
OBJECTIVE
Nonexercise algorithms are cost-effective methods to estimate cardiorespiratory fitness (CRF), but the existing models have limitations in generalizability and predictive power. This study aims to improve the nonexercise algorithms using machine learning (ML) methods and data from US national population surveys.
MATERIALS AND METHODS
We used the 1999-2004 data from the National Health and Nutrition Examination Survey (NHANES). Maximal oxygen uptake (VO2 max), measured through a submaximal exercise test, served as the gold standard measure for CRF in this study. We applied multiple ML algorithms to build 2 models: a parsimonious model using commonly available interview and examination data, and an extended model additionally incorporating variables from Dual-Energy X-ray Absorptiometry (DEXA) and standard laboratory tests in clinical practice. Key predictors were identified using Shapley additive explanation (SHAP).
RESULTS
Among the 5668 NHANES participants in the study population, 49.9% were women and the mean (SD) age was 32.5 years (10.0). The light gradient boosting machine (LightGBM) had the best performance across multiple types of supervised ML algorithms. Compared with the best existing nonexercise algorithms that could be applied to the NHANES, the parsimonious LightGBM model (RMSE: 8.51 ml/kg/min [95% CI: 7.73-9.33]) and the extended LightGBM model (RMSE: 8.26 ml/kg/min [95% CI: 7.44-9.09]) significantly reduced the error by 15% and 12% (P < .001 for both), respectively.
DISCUSSION
The integration of ML and national data source presents a novel approach for estimating cardiovascular fitness. This method provides valuable insights for cardiovascular disease risk classification and clinical decision-making, ultimately leading to improved health outcomes.
CONCLUSION
Our nonexercise models provide improved accuracy in estimating VO2 max within NHANES data as compared to existing nonexercise algorithms.
目的
非运动算法是一种估算心肺功能（CRF）的具有成本效益的方法，但现有的模型在通用性和预测能力方面存在局限性。本研究旨在使用机器学习（ML）方法和来自美国全国人口调查的数据来改进非运动算法。
材料和方法
我们使用了 1999-2004 年全国健康和营养检查调查（NHANES）的数据。最大摄氧量（VO2 max）通过亚最大运动试验测量，是本研究中 CRF 的金标准测量。我们应用了多种 ML 算法来构建 2 个模型：一个使用常用访谈和检查数据的简约模型，以及一个扩展模型，另外还结合了双能 X 射线吸收法（DEXA）和临床实践中标准实验室测试的变量。使用 Shapley 加法解释（SHAP）确定关键预测因子。
结果
在研究人群的 5668 名 NHANES 参与者中，49.9%为女性，平均（SD）年龄为 32.5 岁（10.0）。LightGBM 是多种监督 ML 算法中性能最好的。与可应用于 NHANES 的最佳现有非运动算法相比，简约的 LightGBM 模型（RMSE：8.51 ml/kg/min [95% CI：7.73-9.33]）和扩展的 LightGBM 模型（RMSE：8.26 ml/kg/min [95% CI：7.44-9.09]）分别显著降低了 15%和 12%的误差（均 P < .001）。
讨论
ML 和国家数据源的整合为估算心血管健康提供了一种新方法。这种方法为心血管疾病风险分类和临床决策提供了有价值的见解，最终改善了健康结果。
结论
与现有的非运动算法相比，我们的非运动模型在 NHANES 数据中估计 VO2 max 的准确性有所提高。
Zotero 插件