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一个机器学习框架,用于预测老龄化人口的认知障碍,使用尿金属和人口统计数据

Fengchun Ren¹, Xiao Zhao², Qin Yang³

¹Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.

Frontiers in genetics

|July 10, 2025

概括

环境金属暴露是老年人认知障碍的危险因素. 这项研究使用机器学习来识别关键预测因素,包括,和,并开发了一个网络服务器来进行风险评估.

科学领域:

环境健康环境健康
神经科学是一个神经科学.
计算生物学计算生物学

背景情况:

老年人认知障碍是一个日益严重的公共卫生问题.
环境金属暴露是显著的风险因素,但综合效应和人口影响尚未得到充分理解.

研究的目的:

调查暴露于多种环境金属和老年人认知障碍之间的关联.
使用机器学习识别认知衰退的人口和金属相关预测因素.
开发一个预测模型和风险评估的可访问工具.

主要方法:

在四个NHANES周期 (1999-2014) 中分析了1,230名老年人 (≥60岁) 的数据.
尿中的金属和肌素的量化,用标准化测试评估认知状态.
应用和评估六种机器学习算法,包括极端梯度提升 (XGBoost).

主要成果:

在预测认知障碍方面,XGBoost模型实现了高精度 (ACC=0.81) 和AUC (0.90).
确定的关键预测因素包括教育水平,年龄,种族/种族,肌,以及,和的尿液水平.
为预测模型开发了一个公开可访问的Web服务器.

结论:

机器学习有效地识别了与认知障碍相关的环境金属暴露和人口因素.
开发的网络服务器为老龄化人口的精确预防策略提供了可访问的风险查.

关键词:

这就是 SHAP SHAP 的意思.认知障碍是一种认知障碍.人口统计学人口统计学机器学习是机器学习.金属的金属的金属.

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