机器学习预测高峰氧气吸收和高峰功率输出,以使用非运动特征定制心肺运动测试
Charlotte Wenzel1, Thomas Liebig2, Adrian Swoboda3
1Institute for Sport and Sport Science, Performance and Health (Sports Medicine), TU Dortmund University, Dortmund, Germany.
European journal of applied physiology
|July 3, 2024
概括
机器学习模型可以通过使用非运动数据准确预测峰值氧气吸收和功率输出来个性化心肺运动测试 (CPET) 协议. 这样可以改善CPET.
科学领域:
- 运动生理学 运动生理学
- 机器学习在体育科学中的应用
- 生物医学数据分析
背景情况:
- 心肺运动测试 (CPET) 是评估心肺健康的黄金标准.
- 个性化CPET协议对于准确的评估至关重要.
- 目前的协议个性化方法需要进一步优化.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测峰值氧气吸收 (O2peak) 和峰值功率输出 (PPO).
- 使用非练习功能来个性化CPET道协议.
- 将ML模型的预测精度与多重线性回归 (MLR) 进行比较.
主要方法:
- 一项涉及274名参与者进行的横截面研究,他们在循环人体表上接受CPET.
- 应用各种ML模型 (例如随机森林,梯度提升) 和MLR来预测O2峰和PPO.
- 利用沙普利增量解释 (SHAP) 来识别关键预测特征.
主要成果:
- 随机森林和梯度增强模型在分别预测O2峰和PPO方面表现出卓越的准确性.
- 与MLR相比,ML模型将O2peak和PPO的RMSE (根平均平方误差) 降低了28%,而PPO则降低了22%.
- 身体组成特征,包括骨肌肉质量和细胞外水,被确定为最有影响力的预测因素.
结论:
- 机器学习模型提供了一个比传统的MLR更准确的方法来预测O2峰和PPO.
- 这些ML模型可以有效地个性化CPET道协议.
- 身体成分数据显著提高了预测CPET结果的准确性.
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