基于机器学习的方程,用于改善印度成年人的身体组成估计
Nick Birk1, Bharati Kulkarni2, Santhi Bhogadi3
1Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.
PLOS digital health
|June 23, 2025
概括
结合生物电阻分析 (BIA) 与人体测量措施的新方程,提高了印度成年人身体组成的准确性. 这些新的方法比现有的BIA算法提供了比现有的BIA算法更精确的体脂和瘦肉量估计.
科学领域:
- 人类测量学 人类测量学.
- 身体组成分析 身体成分分析
- 生物电阻分析 (BIA) 的方法
背景情况:
- 生物电阻分析 (BIA) 是在大型研究中评估身体成分的成本效益高的方法.
- 现有的BIA方程可能缺乏对包括南亚人在内的不同人群的概括性.
- 双能X射线吸收计 (DXA) 是一个黄金标准,但对于大规模研究来说,它难以获得.
研究的目的:
- 开发和验证基于机器学习的新型方程,用于预测印度成年人的DXA测量身体成分参数.
- 通过整合简单的人类测量数据来提高BIA测量的准确性.
- 在南亚人群中提高身体成分评估的通用性.
主要方法:
- 结合BIA (TANITA BC-418) 与皮层厚度,身体周长和握力测量.
- 在2632名印度成年人 (1615名男性,1422名女性) 的队列中利用机器学习技术.
- 将数据分为训练 (80%) 和测试 (20%) 集,以开发和验证六个身体组成参数的方程.
主要成果:
- 新式方程显著超过现有的BIA估计算法和传统方程 (例如Durnin-Womersley).
- 总体脂肪质量的平均绝对误差大大减少:0.935公斤 (男性) 和0.976公斤 (女性) 具有新的方程.
- 开发的方程证明了对预测总体脂肪质量,瘦肉质量和各种脂肪/瘦肉质量百分比的有效性有所提高.
结论:
- 补充BIA设备与人体测量措施显著提高了南亚人身体组成评估的有效性.
- 开发的机器学习方法为特定人群的身体组成分析提供了更准确和更容易使用的方法.
- 这种方法可以扩展到其他BIA设备和群体,以提高BIA技术的性能和适用性.
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