基于机器学习的预测CAC定义的心血管风险使用例行健康检查数据:在台湾人口的回顾性横截面研究
Shan-Shan Chuang1, Fu-Tien Chiang2, Shih-Wei Lin3
1St. Luke Health Management Center, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City, Taiwan; Graduate Institute of Management, Chang Gung University, Taoyuan, Taiwan.
Journal of the Formosan Medical Association = Taiwan yi zhi
|February 4, 2026
概括
机器学习模型有效地使用例行健康检查数据预测心血管风险. 这些工具为预防性心脏病学提供了可扩展的预先查,特别是在先进资源有限的地方.
科学领域:
- 预防性心脏病学 预防性心脏病学
- 机器学习应用 机器学习应用
- 心血管风险分层的分层.
背景情况:
- 早期发现心血管风险对于预防性心脏病学至关重要.
- 机器学习 (ML) 为风险分层提供了一个可扩展的,非侵入性的方法.
研究的目的:
- 评估ML模型在预测冠状动脉 (CAC) 定义的心血管风险方面的有效性.
- 评估例行健康检查数据对心血管风险预测的有用性.
主要方法:
- 追溯分析了899名无症状成年人接受CAC扫描或CCTA.
- 训练有素的决策树 (DT),随机森林 (RF) 和支持矢量机 (SVM) 分类器使用19个人口/临床变量.
- 使用精度和曲线下的面积 (AUC) 评估模型性能.
主要成果:
- 射频模型实现了最高的性能 (精度:76%;AUC:0.78).
- SVM和DT模型也表明了临床上有意义的歧视 (AUC分别为0.78和0.75).
- 所有模型都有效地利用了可访问的非实验室健康检查数据.
结论:
- 整合常规健康检查数据的ML模型可以准确预测CAC定义的心血管风险.
- 这些模型显示了在预防性医疗保健中作为实用,可扩展的预选工具的潜力.
- 它们在资源有限的环境中特别有价值.
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