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一个可解释的机器学习模型与人口变量和饮食模式用于ASCVD识别:来自美国NHANES 1999-2018

Qun Tang¹, Yong Wang¹, Yan Luo²

¹Department of Cardiovascular Medicine, Wuhu City Second People's Hospital, Wuhu, 241000, China.

BMC medical informatics and decision making

|March 3, 2025

概括

机器学习模型确定了动脉样硬化心血管疾病 (ASCVD) 风险的关键因素. 男性的性别,年龄和吸烟增加了风险,而乳制品摄入量减少了风险,为ASCVD预防提供了洞察力.

科学领域:

心血管健康心血管健康
机器学习应用机器学习应用
营养流行病学营养流行病学

背景情况:

关于与动脉样硬化心血管疾病 (ASCVD) 的人口和饮食联系的研究有限.
了解这些关联对于制定有针对性的预防策略至关重要.
现有的模型在预测ASCVD风险时往往缺乏透明度和准确性.

研究的目的:

开发和验证一个透明的机器学习 (ML) 算法来预测ASCVD.
确定与ASCVD相关的显著的人口和饮食因素.
分析使用大型,代表性美国人口数据集的相关性.

主要方法:

利用了美国国家健康和营养检查调查 (1999-2018) 的数据,其中有40,298名参与者.
开发并比较了五种ML模型来预测ASCVD风险.
选择了Extreme梯度提升 (XGBoost) 模型,因为它的性能优越.

主要成果:

XGBoost模型在预测ASCVD时实现了0.8143的AUC和88.4%的准确性.
确定了男性性别,年龄,吸烟和ASCVD风险之间的正相关性.
发现乳制品摄入量的负相关性;低精制谷物摄入量没有降低风险;贫困收入比率和卡路里摄入量的非线性关联.

结论:

XGBoost模型有效地确定了ASCVD的人口和饮食预测因素.
男性性别,年龄较大和吸烟是显著的危险因素.
饮食因素,如乳制品摄入量发挥作用,突出了个性化营养指导在ASCVD预防中的潜力.

关键词:

动脉样硬化心血管疾病.人口学变量人口学变量饮食模式饮食模式机器学习机器学习尼汉斯 (NHANES) 是一个名人.

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