使用机器学习构建与环境化学物质相关的高血压的预测模型
Shanshan Liu1,2, Lin Lu3, Fei Wang1
1Health Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, No. 28, Fuxing Road, Haidian District, Beijing, 100853, China.
Environmental science and pollution research international
|December 17, 2023
概括
环境化学物质暴露与高血压有关,这是一种严重的心血管疾病. 机器学习模型将特定的化学物质 (如丁) 确定为关键预测因素,为向治疗铺平了道路.
科学领域:
- 环境健康 环境健康
- 心血管疾病流行病学
- 计算生物学 计算生物学
背景情况:
- 高血压是一个重要的全球健康问题,有许多并发症.
- 环境暴露越来越被认为是导致慢性疾病的潜在因素.
- 了解特定的环境污染物和高血压之间的联系对于公共卫生至关重要.
研究的目的:
- 调查环境污染物暴露与高血压之间的关系.
- 用机器学习评估环境暴露对高血压的预测值.
- 确定与高血压风险相关的关键环境化学物质.
主要方法:
- 利用了2005-2012年国家健康和营养调查 (NHANES) 的数据.
- 应用特征选择和规范网络分析,以确定相关的环境化学物质和共变量.
- 采用机器学习模型,包括XGBoost,随机森林,物流回归,MLP和SVM用于高血压预测.
- 用了SHapley添加式解释 (SHAP) 来实现模型的解释性.
主要成果:
- 总共有29个变量,包括21种化学物质,被选择用于机器学习分析.
- 开发的机器学习模型展示了高血压的预测能力,AUC从0.721到0.731.73不等.
- 丁甲 (BUP),甲 (PPB) 和9-氧 (P17) 被确定为高血压的最重要的预测因素.
结论:
- 环境暴露在高血压的发展中起着重要作用.
- 特定的化学物质暴露,如 parabens 和 9-hydroxyfluorene,与高血压风险增加有关.
- 优化的机器学习模型显示出基于环境暴露档案预测高血压的前景,有助于开发有针对性的干预措施.
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