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预测暴露于重金属的成年人心血管疾病风险:可解释机器学习

Meiyue Shen¹, Yine Zhang², Runqing Zhan³

¹Department of Epidemiology and Health Statistics, School of Public Health, Qingdao University, Qingdao 266071, China.

Ecotoxicology and environmental safety

|December 25, 2024

概括

接触重金属,包括和,显著增加心血管疾病 (CVD) 的风险. 机器学习模型确定了心血管疾病的重金属预测因素,有助于个性化风险评估.

科学领域:

环境健康环境健康
心血管医学心血管医学
机器学习机器学习

背景情况:

暴露于重金属是越来越严重的公共卫生问题.
了解重金属与心血管疾病 (CVD) 之间的联系对于预防至关重要.
机器学习为分析复杂的健康数据集提供了强大的工具.

研究的目的:

开发一个可解释的机器学习模型来研究重金属暴露与心血管疾病之间的关联.
确定特定的重金属及其预测心血管疾病风险的暴露水平.
加强心血管疾病预测模型,以改善患者护理和资源配置.

主要方法:

利用来自4600名成年人的国家健康和营养检查调查 (NHANES) 的数据.
用于特征选择的使用最小绝对收缩和选择运算符 (LASSO) 回归.
构建并比较了六种机器学习模型:随机森林,决策树,梯度提升决策树,K-最近邻居,支持向量机器和AdaBoost.
综合特征重要性,部分依赖图,以及Shapley增量解释 (SHAP) 模型可解释性.

主要成果:

随机森林模型实现了最高的性能 (90%的准确性,0.85AUC,0.86F1得分).
心血管疾病的主要预测因素包括尿中的 (Cd),血液中的 (Pb),尿中的 (Tl) 和尿中的 (W).
尿液Cd,血液Pb和尿液W的较高水平与心血管疾病风险增加有关,而尿液Tl水平较低与风险降低相关.
成为CVD风险的主要决定因素.

结论:

一个可解释的随机森林模型有效地根据重金属暴露预测心血管疾病风险.
特定的重金属如,和是心血管疾病的重要贡献者.
研究结果支持个性化患者护理和针对重金属暴露的人群的有针对性的干预措施.

关键词:

在 AdaBoost 中使用 AdaBoost.心血管疾病是什么心血管疾病重金属是一种重金属.机器学习是机器学习.部分依赖的图表部分依赖图表随机的森林随机的森林

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