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一个基于可解释机器学习的中风风险预测模型

Qinggui Li¹, Xiao Wang², Qian Ye¹

¹Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

|October 7, 2025

概括

机器学习模型有效预测中风风险. 随机森林算法分析了运动频率和小牛周长等因素,显示出高精度,有助于临床决策.

科学领域:

医疗信息学医疗信息学
医疗保健中的机器学习
心血管疾病预测预测

背景情况:

在全球范围内,中风是导致死亡和残疾的主要原因.
准确预测中风风险对于及时干预和预防至关重要.
现有的风险评估工具可能无法完全捕捉中风病因的复杂性.

研究的目的:

开发和评估用于预测中风风险的机器学习模型.
确定导致中风发病率的关键风险因素.
评估各种算法在中风风险预测中的性能.

主要方法:

对134名中风患者和354名对照患者的回顾性分析.
开发和比较八个机器学习模型,包括随机森林 (RF).
使用LASSO和后勤回归进行特征选择;通过ROC/PR曲线和准确度指标进行模型评估.

主要成果:

射频算法实现了高性能:ROC AUC为0.96,PR AUC为0.92,特异性为0.97,精度为0.92.
确定的主要预测因素包括每周运动日,小腿周长,病史,性别,BMI和STRATIFY分数.
沙普利补充解释证实了体力活动和人类测量对中风风险的重大影响.

结论:

机器学习,特别是射频算法,为中风风险预测提供了强大的方法.
定期的体力活动和特定的生理标记是中风风险的重要决定因素.
这些预测模型可能会增强临床决策和个性化中风预防策略.

关键词:

机器学习是机器学习.身体活动身体活动预测模型预测模型一次性中风中风中风中风中风

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