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使用区域医疗保健大数据和机器学习开发和验证中风风险预测模型

Yunxia Duan^1,2, Rui Wang^1,3, Yumei Sun¹

¹School of Nursing, Peking University, Beijing, China.

International journal of nursing sciences

|December 10, 2025

概括

与传统方法相比,机器学习模型显著改善了中风风险预测. 随机森林模型表现出卓越的准确性,为社区中风预防提供了有价值的工具.

科学领域:

心血管疾病的研究研究.
医疗信息学医疗信息学
预测建模的预测建模.

背景情况:

在全球范围内,中风仍然是导致残疾和死亡的主要原因.
准确的风险预测对于有效的初级预防策略至关重要.
传统模型可能无法充分利用复杂的医疗保健大数据.

研究的目的:

利用区域医疗保健大数据开发和验证机器学习 (ML) 中风风险预测模型.
将ML模型的预测性能与传统的物流回归 (LR) 模型进行比较.
为了确定中风的关键可修改的风险因素.

主要方法:

使用CHERRY数据库 (2015-2021) 的回顾性队列研究,共计92,172名参与者.
使用LR和ML算法 (DT,RF,XGBoost,BPNN) 开发预测模型.
通过ROC曲线,校准和混矩阵进行性能评估;变量重要性分析.

主要成果:

随机森林 (RF) 模型实现了最高的精度 (0.935),灵敏度 (0.947) 和F1得分 (0.935).
ML模型显著优于LR,RF显示最高的曲线下面积 (AUC) 为0.988 (训练) 和0.980 (验证).
关键预测因素包括高血压,年龄,糖尿病和缩血压.

结论:

基于ML的模型,特别是RF,与LR相比,提供了优越的中风风险预测.
开发的RF模型作为一种有效的工具,用于在初级中风预防中风风险分层.
识别可修改的风险因素,如高血压和糖尿病是干预的关键.

关键词:

大数据就是大数据.机器学习机器学习护理护理护理预测模型的预测模型.一次性中风,中风.

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