通过心率和机器学习增强痴呆风险预测在加拿大长度老龄研究中的心率和机器学习
Shakiru A Alaka1, So-Fong Cam Ngan1, Rebecca Ek MacPherson1,2
1Department of Health Science, Faculty of Applied Health Sciences, Brock University, St Catharines, Ontario, Canada.
Journal of Alzheimer's disease : JAD
|October 29, 2025
概括
将静止心率 (RHR) 添加到心血管风险因素,衰老和痴呆发病率 (CAIDE) 模型中,可以轻微改善痴呆风险预测. 这种简单的补充增强了早期识别患痴呆风险的个人的能力.
科学领域:
- 老年学是指老年学的学科.
- 神经学 神经学
- 生物统计学 生物统计学
背景情况:
- 有效的痴呆症管理依赖于准确和可访问的风险评估工具.
- 心血管风险因素,衰老和痴呆发病率 (CAIDE) 模型是中年痴呆风险评估的标准.
研究的目的:
- 评估是否结合静止心率 (RHR) 可以提高CAIDE模型对痴呆风险的预测准确度.
- 利用机器学习方法改善痴呆风险预测.
主要方法:
- 来自加拿大长度老龄化研究的27,768名参与者的分析,以预测3年痴呆风险.
- 使用随机森林和支持矢量机算法的预测模型的开发.
- 使用AUC,MCC和Brier得分等指标进行绩效评估,并进行内部交叉验证.
主要成果:
- 当RHR被添加到CAIDE模型中时,观察到痴呆风险预测的适度但显著改善.
- 使用RHR的随机森林模型在训练和测试数据集中显示了AUC和MCC值的改善.
- 支持向量机也表明,在RHR纳入的情况下,AUC和MCC增加了2-3%.
结论:
- 将RHR纳入CAIDE模型可以显著提高预测性能.
- 这种非侵入性,常规可用的数据点有助于更早地识别患痴呆风险的个体.
- 这些发现支持为临床和社区环境开发可扩展和实用的痴呆风险查工具.
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