基于机器学习的风险因素分析和预测模型构建用于慢性心力衰竭死亡率
Qian Xu1,2, Ruicong Yu2, Xue Cai3
1Zhongda Hospital, Southeast University, Nanjing, China.
Journal of global health
|September 12, 2025
概括
机器学习模型可以使用健康生态因素来预测慢性心力衰竭死亡风险. 一个极端梯度增强模型实现了81.58%的准确性,识别了关键的个人和环境预测因素.
科学领域:
- 心脏病学 心脏病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 慢性心力衰竭 (CHF) 构成了全球严重的死亡负担.
- 传统的CHF风险预测工具缺乏准确性和全面性.
- 机器学习 (ML) 和健康生态框架为改善风险预测提供了潜力.
研究的目的:
- 开发一个基于ML的模型来预测CHF死亡率.
- 用健康生态框架分析CHF死亡风险因素.
- 将健康生态理论与ML集成在一起,以系统地识别风险因素.
主要方法:
- 利用了489名CHF患者的数据,并对其进行了10年死亡随访.
- 应用了五层健康生态框架来选择58个变量.
- 使用SMOTE-ENN用于数据不平衡和XGBoost用于死亡率预测,通过十倍交叉验证进行验证.
主要成果:
- 确定了24个关键的死亡风险因素,涉及个人特征,行为和生活条件.
- SMOTE-ENN和XGBoost模型实现了81.58%的精度和0.83.8的AUC.
- 关键预测因素包括年龄,BMI,药物使用,血压,代谢标志物和环境因素.
结论:
- 通过整合健康生态学和ML成功归类了CHF死亡风险因素.
- 开发的模型显示高精度,但需要进一步优化临床应用.
- 这种方法提高了对CHF死亡率的多维风险因素的理解.
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