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鉴定使用制阻燃剂暴露的肺风险:基于SHAP方法的机器学习预测模型

Qihang Xie¹, Haoran Qu¹, Jianfeng Li¹

¹Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Frontiers in public health

|July 10, 2025

概括

这项研究开发了一种机器学习模型,使用制阻燃剂 (BFRs) 暴露和人口统计数据来预测肺气风险. 更高的BFR暴露,特别是PBB153,与肺瘤风险增加有关.

科学领域:

环境健康环境健康
肺部医学肺部医学
计算生物学计算生物学

背景情况:

肺显著导致肺部疾病的进展和相关的健康风险.
环境暴露,如制阻燃剂 (BFRs),是潜在的肺气风险因素,但研究不足.
在肺瘤预测中BFRs的作用仍然在很大程度上被忽视.

研究的目的:

开发一个机器学习 (ML) 模型来预测肺风险.
将BFR暴露数据和人口特征纳入预测模型.
评估BFRs对肺气风险预测的影响.

主要方法:

使用了NHANES (2005-2016) 数据集,其中有8205名参与者.
在训练 (70%) 和测试 (30%) 集上应用了八个ML算法 (lightGBM,MLP,DT,KNN,RF,SVM,ENET,XGBoost).
雇佣的SHAP和部分依赖地图 (PDP) 为模型的解释性.

主要成果:

MLP模型实现了最高的性能,AUC为0.83.
年龄和PBB153被确定为最有影响力的预测因素.
较高的BFR暴露,特别是PBB153,显示出与肺气风险增加的强烈关联,由SHAP和WQS分析证实.

结论:

对于肺风险评估,BFR暴露具有显著的预测价值.
纳入像BFR这样的环境因素对于准确的疾病预测模型至关重要.
研究结果支持将BFR评估纳入个性化健康风险评估和公共卫生战略.

关键词:

这就是 SHAP SHAP 的意思.制阻燃剂是一种制阻燃剂.突发性质炎症是强调性的.环境暴露环境暴露环境暴露机器学习是机器学习.

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Chronic Obstructive Pulmonary Disease II: Emphysema

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