在西太平洋地区增强心血管风险预测:根据马来西亚人口量身定制的机器学习方法
Sazzli Kasim1,2,3,4, Putri Nur Fatin Amir Rudin5, Sorayya Malek5
1Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.
PloS one
|June 17, 2025
概括
机器学习模型在马来西亚显著改善了心血管疾病 (CVD) 风险预测,表现优于传统得分. 这一进步为亚洲人群中心血管疾病预防提供了更好的决策.
科学领域:
- 心血管健康的心血管健康
- 机器学习应用程序 机器学习应用程序
- 亚洲的公共卫生
背景情况:
- 心血管疾病 (CVD) 在马来西亚和西太平洋地区构成了重大公共卫生挑战.
- 现有的风险预测模型可能无法完全捕捉不同亚洲人群中心血管疾病风险的细微差别.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测马来西亚队列中的10年心血管疾病风险.
- 建立适用于具有类似遗传和环境因素的其他亚洲人口的预测模型.
主要方法:
- 在REDISCOVER注册表 (5,688名参与者) 的数据上训练了多个ML算法,包括后勤回归 (LR),支持向量机 (SVM),随机森林 (RF) 和极端梯度提升 (XGBoost).
- 使用70:30列车测试分割与5倍交叉验证,并使用曲线下的面积 (AUC),灵敏度,特异性和净重新分类指数 (NRI) 评估模型.
- 将ML模型的性能与传统的风险评分进行比较,例如弗雷明汉风险评分 (FRS) 和修订的聚合队列方程 (RPCE).
主要成果:
- 后勤回归 (LR) 模型实现了0.77的最高AUC,超过了FRS (0.72) 和RPCE (0.74).
- ML模型显示NRI的显著改善 (13.15%比FRS,7.00%比RPCE),表明风险重新分类的增强.
- 通过SHAP分析发现的关键预测因素包括静缩血压,体重和腰围. 最好的模型是在Web平台上部署的.
结论:
- 机器学习模型在改善马来西亚心血管疾病风险分层方面是有效的.
- 这些ML模型增强了预防心血管疾病的临床决策,并可以作为其他亚洲人口的典范.
- 开发的ML工具提供实时风险预测,促进及时干预并改善患者的治疗结果.
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