机器学习与传统统计数据之间的心血管疾病特异性死亡率的侵入性和非侵入性变量预测模型
Seonggyu Choi1, Minsuk Oh1,2,3, Dong Hoon Lee1
1Department of Sports Industry Studies, Yonsei University, Seoul, South Korea.
Scientific reports
|October 8, 2025
概括
预测心血管疾病 (CVD) 死亡率是可以使用单独的非侵入性指标. 机器学习模型比传统方法提供了稍微更好的预测,而不需要血液脂质配置文件.
科学领域:
- 心脏病学 心脏病学
- 生物统计学 生物统计学
- 医疗保健中的机器学习
背景情况:
- 心血管疾病 (CVD) 仍然是全球主要的死亡原因.
- 准确预测心脏病特异性死亡率对于及时干预至关重要.
- 传统的统计模型通常依赖于侵入性生物标志物,因此需要进一步研究非侵入性预测因素.
研究的目的:
- 用非侵入性指标评估心血管疾病 (CVD) 特定死亡率的预测性能.
- 将传统的统计模型与用于预测心血管疾病死亡率的机器学习方法进行比较.
- 为了确定是否结合血脂概况显著提高预测准确性.
主要方法:
- 利用了1,749,444名韩国成年人的数据,对心血管疾病特异性死亡率进行了10年的随访.
- 采用传统的考克斯比例危险模型和机器学习模型 (随机生存森林,梯度增强生存,生存树).
- 使用曲线下面面积 (AUC),c指数和布里尔得分的模型性能比较,带有和没有侵入性变量 (甘油三,禁食葡萄糖,胆固醇).
主要成果:
- 所有仅使用非侵入性预测因素 (性别,年龄,腰与身高比,糖尿病,高血压,体力活动) 的模型都实现了AUC>0.800.
- 非侵入性模型的表现与包括血脂概况在内的模型相提并论.
- 与传统模型相比,机器学习模型随着时间的推移显示出略高的预测性能,尽管差异并不显著.
结论:
- 非侵入性指标足以有效预测心脏病特异性死亡率.
- 机器学习模型在预测准确度方面提供了轻微的,但不实质性的改进.
- 添加侵入性血脂概况并没有显著提高心血管疾病死亡率模型的预测性能.
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