对动脉样性心血管疾病的生活方式风险因素的基于机器学习的分析:回顾性病例对照研究
Hye-Jin Kim1, Heeji Choi1, Hyo-Jung Ahn2
1Artificial Intelligence Research Center, College of Medicine, Hallym University, Chuhcneon-si, Republic of Korea.
JMIR medical informatics
|August 7, 2025
概括
结合生活方式因素的机器学习模型显著改善了动脉样硬化心血管疾病 (ASCVD) 风险预测. 基于个人生活方式行为的个性化预防策略可以有效地降低ASCVD风险.
科学领域:
- 心脏病学 心脏病学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 动脉样硬化心血管疾病 (ASCVD) 的风险受生活方式和慢性疾病的影响.
- 个人的ASCVD风险有很大的差异.
研究的目的:
- 评估机器学习 (ML) 模型对ASCVD风险的预测准确度.
- 将生活方式风险行为纳入ML模型以提高预测.
- 在此分析中使用韩国全国卫生数据库.
主要方法:
- 利用来自韩国国家健康和营养检查调查 (KNHANES) 的数据.
- 应用了五种ML算法:逻辑回归 (LR),支持向量机,随机森林,极端梯度增强和光梯度增强.
- 员工倾向分数匹配 (PSM) 用于人口混调整和沙普利增量解释 (SHAP) 用于变量重要性.
主要成果:
- 极端梯度增强模型实现了接收器运行特性曲线 (AUC) 下的最高面积.
- 光梯度增强模型在精度,回忆和F1得分方面表现出卓越的表现.
- 确定的主要预测因素包括吸烟,年龄,BMI,欧米茄-3摄入量和LDL胆固醇.
结论:
- 与传统方法相比,结合生活方式因素的ML模型可以提高ASCVD风险预测.
- 针对生活方式修改的个性化预防策略可以有效地减轻ASCVD风险.
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