心血管疾病的保护性预测因素:一种可解释的AI方法
Minh H N Le1, Hien Quang Kha2, Han H Huynh3
1International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan; AIBioMed Research Group, Taipei Medical University, Taipei, 110, Taiwan; Cardiovascular Research Laboratories, Methodist Hospital, Merrillville, IN, 46410, USA.
Public health
|November 28, 2025
概括
机器学习模型确定了预防心血管疾病 (CVD) 的关键保护因素. 年龄较小,收入较高,保险和没有糖尿病或抑郁症是心血管弹性的强有力的预测因素.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 心血管疾病研究研究
背景情况:
- 传统的心血管疾病 (CVD) 风险评分在不同人群中都有局限性.
- 需要可解释的模型来识别对心血管疾病的保护因素.
研究的目的:
- 使用国家调查数据开发可解释的机器学习 (ML) 模型.
- 为了确定预防心血管疾病 (CVD) 的保护因素.
主要方法:
- 来自2021年行为风险因素监测系统 (BRFSS) 的116,608个成人记录的横截面分析.
- 在人口和行为特征上训练了三个ML模型 (XGBoost,CNN,随机森林).
- 为了模型的可解释性,利用了夏普利添加式解释 (SHAP).
主要成果:
- 在预测心血管疾病风险方面,XGBoost表现出卓越的性能.
- 发现的关键保护因素包括年龄较小,收入较高,保险覆盖,糖尿病和抑郁症的不存在.
- SHAP分析证实了这些因素的重要性.
结论:
- 一个可解释的XGBoost模型有效地预测了心血管弹性.
- 调查结果强调了社会经济地位,心理健康和缺乏慢性疾病对预防心血管疾病的重要性.
- 支持积极主动,公平的CVD预防策略和资源分配.
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