在亚洲人群中提高心血管风险预测:与数字健康平台集成的机器学习方法
Sazzli Kasim1,2,3, Putri Nur Fatin Amir Rudin4, Xue Ning Kiew4
1Cardiovascular Advancement and Research Excellence Institute (CARE Institute), Universiti Teknologi MARA, Selangor, Malaysia.
概括
机器学习模型在马来西亚队列中显著改善了心血管疾病风险预测,表现优于传统得分. 对于更广泛的东南亚人口,需要进一步验证.
科学领域:
- 心血管疾病研究研究
- 医疗保健中的机器学习
- 流行病学 流行病学
背景情况:
- 心血管疾病 (CVD) 在东南亚是一个重大的健康负担.
- 现有的心血管疾病风险预测模型可能不准确地反映区域人口特征.
- 开发本地化,准确的风险评估工具对于有效的预防策略至关重要.
研究的目的:
- 开发和验证基于机器学习 (ML) 的模型,用于心血管疾病 (CVD) 风险预测.
- 评估ML模型的性能与马来西亚队列中已建立的风险得分相比.
- 确定区域相关的变量,以改善心血管疾病风险分层.
主要方法:
- 分析了来自REDISCOVER研究 (n=10,044) 的数据,其中有4,299例.
- 发展物流回归,随机森林和支持向量机模型.
- 使用递归特征消除 (RFE) 和顺序逆向消除 (SBE) 进行特征选择.
- 使用曲线下的面积 (AUC),灵敏度,特异性,校准和净重新分类指数 (NRI) 的模型性能评估.
主要成果:
- 具有SBE选择特征的支持矢量机 (SVM) 模型实现了0.800.00的最高AUC.
- 与弗雷明汉风险评分 (FRS),修订的聚合队列方程 (RPCE) 和世卫组织心血管疾病图表相比,ML模型表现出优异的性能.
- 与FRS和RPCE相比,在净重新分类指数 (NRI) 中观察到显著改善 (p < 0.00001).
- 普拉特缩放用于减轻ML模型校准中观察到的初始过度预测.
结论:
- 结合特定区域变量的机器学习模型为心血管疾病风险提供了增强的歧视和重新分类.
- 开发的ML模型显示了改善CVD风险预测在马来西亚和潜在的其他东南亚人群的前景.
- 建议对不同东南亚群体进行外部验证,以确认可通用性.
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