使用机器学习技术预测瘦身质量,尾瘦身质量和尾骨肌肉质量的建模:使用NHANES数据和Look AHEAD研究的综合分析
Daniel Olshvang1, Carl Harris1, Rama Chellappa1,2
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.
PloS one
|September 6, 2024
概括
新的机器学习模型准确地预测瘦身量 (LBM) 和相关指标,有助于检测肉症. 这些模型为评估肌肉健康提供了DXA扫描的有希望的替代方案.
科学领域:
- 老年学和营养科学研究
- 生物医学信息学 生物医学信息学
- 临床研究 临床研究
背景情况:
- 标志着肌肉损失的萨尔科佩尼亚对健康构成重大风险,特别是在老年人和患有慢性疾病的人群中.
- 目前用于评估瘦质量的方法,如双能X射线吸收计 (DXA),其可访问性有限,延迟了关键干预.
- 准确预测瘦身质量 (LBM),尾瘦身质量 (ALM) 和尾骨肌量 (ASMM) 对于早期发现和管理肉类至关重要.
研究的目的:
- 开发和验证用于预测LBM,ALM和ASMM的机器学习模型.
- 用人体测量和人口统计数据评估各种机器学习算法的预测性能.
- 探索骨矿物质密度在增强瘦肉质预测模型中的实用性.
主要方法:
- 利用了来自国家健康和营养检查调查 (NHANES) 和糖尿病健康行动 (Look AHEAD) 研究的数据.
- 训练有素的机器学习模型在人体测量数据,人口因素和DXA衍生的指标上.
- 评估了包括LassoNet在内的多个算法,以评估它们在估计正常化LBM,ALM和ASMM中的准确性.
主要成果:
- 机器学习模型在各种算法中展示了一致的预测性能.
- 拉索网在预测瘦质量指标方面表现出卓越的准确性.
- 结合骨矿物密度测量并没有显著提高预测准确度,建议使用无DXA的替代品.
结论:
- 开发了强大的机器学习模型,能够准确预测关键的瘦质量组件.
- 这些发现表明,在广泛的瘦肉质量评估中,非DXA基方法的潜力很大.
- 这些模型为改善肉症管理和个性化健康干预提供了有希望的途径,这证明了在不同人群中进一步验证的必要性.
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