尿路重金属与美国成年人骨质疏松症的关联使用可解释机器学习
Weihuan Huang1, Dongpei Liu2, Gang Liu2
1Department of Joint and Sports Medicine, the Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China; Postgraduate College, Dalian Medical University, Dalian, China.
Toxicology letters
|February 5, 2026
概括
接触重金属,特别是 (Tl),与骨质疏松症有关. 机器学习模型将年龄和性别确定为关键因素,XGBoost显示出最好的预测性能.
科学领域:
- 环境健康 环境健康
- 生物统计学 生物统计学
- 毒理学 毒理学 毒理学
背景情况:
- 环境重金属暴露是与骨退化相关的公共卫生问题.
- 有证据表明,重金属与骨折风险增加之间存在相关性.
- 本研究使用机器学习研究重金属暴露和骨质疏松症之间的关系.
研究的目的:
- 分析国家健康和营养调查 (NHANES) 数据,以了解重金属暴露和骨质疏松症.
- 开发和比较九种用于预测骨质疏松风险的机器学习模型.
- 确定与骨质疏松症相关的关键环境和人口因素.
主要方法:
- 利用2003-2018年的NHANES数据,使用斯皮尔曼相关性和Boruta算法进行变量选择.
- 使用SMOTE进行数据平衡后应用机器学习模型,包括XGBoost,随机森林和神经网络.
- 评估模型性能使用AUC,精度,灵敏度,特异性,精度和F1得分,并使用SHAP进行解释.
主要成果:
- XGBoost模型实现了最高的性能 (AUC=0.834),超过了其他八种模型.
- 年龄是主要的预测因素 (平均0.30),其次是特定的重金属, (Tl) 显示最强的关联.
- 该模型确定了, (Pb) 和 (Cd) 作为骨质疏松症预测的重要贡献者.
结论:
- 在预测与重金属暴露相关的骨质疏松风险方面,XGBoost表现出卓越的性能.
- (Tl) 成为最重要的尿液金属预测剂,年龄和性别也是关键因素.
- 建议使用先进的算法进行进一步的研究,以验证这些发现并提高预测精度.
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