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解锁链接:利用机器学习预测心血管疾病风险,重点关注空气流阻塞
Xiyu Cao1, Jianli Ma1, Xiaoyi He2
1Department of Respiratory Medicine, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
BMC medical informatics and decision making
|February 3, 2025
概括
空气流阻塞 (AO) 显著增加了心血管疾病 (CVD) 的风险. 机器学习模型准确地预测心血管疾病,确定年龄,高血压和社会经济因素作为早期干预的关键预测因素.
科学领域:
- 心脏病学和呼吸系统医学
- 数据科学和机器学习
背景情况:
- 心血管疾病 (CVD) 和呼吸道疾病经常同时存在.
- 空气流阻塞 (AO) 的严重程度与心血管疾病发病率和死亡率有关.
- 当前的CVD风险模型没有充分考虑AO作为一个独立的风险因素.
研究的目的:
- 开发和验证用于预测心血管疾病风险的机器学习 (ML) 模型.
- 评估AO对CVD风险的独立贡献.
- 在一般人群和AO个体中确定心血管疾病的关键预测因子.
主要方法:
- 使用了NHANES III和NHANES 2007-2012年40岁以上参与者的数据集,并提供完整的AO和CVD数据.
- 使用后勤回归来分析AO-CVD关联和六个ML模型 (XGBoost通过RandomizedSearchCV优化) 来预测心血管疾病风险.
- 评估模型使用AUC,准确性,精度,回忆,F1得分,Brier得分和SHAP进行解释.
主要成果:
- 在AO和CVD患病率之间发现了显著的正相关性 (P < 0.05).
- 在XGBoost模型中,在一般人群中 (AUC=0.7508) 和那些有AO的人群中 (AUC=0.6645) 证明了最佳的心血管疾病风险预测.
- 对于一般人群来说,关键预测因素是年龄,高血压和PIR;对于AO患者来说,教育,性别和种族是最具影响力的.
结论:
- AO是心血管疾病的重要独立风险因素.
- 机器学习模型,特别是XGBoost,有效地预测心血管疾病风险,将AO作为预测因素.
- 识别关键的人口和临床因素可以促进早期心血管疾病风险评估和风险人群的干预.
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