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机器学习和孟德尔随机化确定冠心病的关键生活方式因素:基于NHANES的研究

Yang Yang Cui¹, Yonghong Zhang¹, Lang Zeng¹

¹Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.

International journal of cardiology. Cardiovascular risk and prevention

|November 24, 2025

概括

机器学习使用生活方式因素准确预测冠心病 (CHD) 风险. 支持矢量机 (SVM) 显示出最佳性能,识别了像BMI和吸烟这样的关键因果因素,以准确预防.

科学领域:

心血管疾病流行病学
医疗保健中的机器学习
遗传流行病学遗传流行病学

背景情况:

冠心病 (CHD) 仍然是全球死亡的主要原因.
对心血管疾病的预测建模往往缺乏因果推断,限制了有针对性的预防策略.
将生活方式数据与高级分析集成,可以改善心血管疾病风险评估.

研究的目的:

使用国家健康和营养检查调查 (NHANES) 数据,比较多种机器学习模型对心脏病的预测性能.
通过孟德尔随机化 (MR) 识别具有预测能力和对心脏病的因果影响的生活方式变量.
在心血管疾病研究中弥合预测建模和因果推理之间的差距.

主要方法:

用了NHANES数据 (2013-2018) 来分析人口统计和生活方式变量.
使用递归特征消除 (RFE) 进行特征选择和八个机器学习模型 (SVM,NN,NB,XGBoost,MLP,GLM,AdaBoost,DT) 进行CHD预测.
应用孟德尔随机化 (MR) 来验证通过全基因组关联研究 (GWAS) 仪器识别的顶级预测者的因果关联.

主要成果:

RFE确定了年龄,性别,禁食血糖,BMI,总胆固醇 (TC) 摄入量,睡眠时间,腹压血压和吸烟作为显著的CHD预测因素.
支持向量机 (SVM) 实现了最高的预测准确度 (83.4%) 和AUC (0.909).
MR证实了BMI,TC摄入量,失眠,透气血压和吸烟的因果关系.

结论:

SVM模型提供了一个临床可操作的工具,用于使用NHANES数据高效预测CHD.
关键的生活方式因素,包括年龄,性别,BMI,TC摄入量,睡眠时间,透静血压和吸烟显著影响心脏病风险.
将预测建模与遗传证据相结合,可以提高心血管疾病的精确预防策略.

关键词:

因果推理的原因推理.冠心病是一种冠心病.机器学习是机器学习.门德尔的随机化尼汉斯 (NHANES) 是一个名人.

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