一个动态加权的集体学习框架,用于预测2型糖尿病心血管风险:基于SHAP的可解释性的比较研究
ChunHong Yuan1,2, Ziyang Liu3, Xiangyu Li4
1School of Rehabilitation Medicine, Qilu Medical University, Zibo, 255300, China.
Scientific reports
|November 22, 2025
概括
结合传统中医 (TCM) 舌头诊断和现代生物标志物的新组合模型改善了糖尿病心血管风险预测. 这种方法为糖尿病并发症提供了更高的准确性和个性化的干预措施.
科学领域:
- 心血管健康 心血管健康
- 糖尿病研究研究 糖尿病研究
- 综合医学是一个整体的医学.
背景情况:
- 心血管并发症是糖尿病患者的主要死亡原因.
- 传统的风险评估模型在捕捉复杂,非线性关系和在不同人群中概括方面存在局限性.
- 现有的模型往往无法有效地整合各种诊断指标.
研究的目的:
- 开发和评估一种新的多指数动态加权整体模型,用于预测2型糖尿病的心血管并发症.
- 将传统中医 (TCM) 舌头诊断索引与现代医学生物标志物相结合.
- 提高糖尿病风险评估的准确性和通用性.
主要方法:
- 一项横截面研究招募了3,111名2型糖尿病患者,其中2895人被纳入最终分析.
- 使用随机森林 (RF),梯度增强决策树 (GBDT),K-最近邻居 (KNN) 和极端梯度增强 (XGBoost) 算法构建了基准模型.
- 开发了一个动态加权整体框架,以优化各种诊断索引的整合.
主要成果:
- 整体模型实现了高性能指标:95.68%的准确性,94.92%的灵敏性和96.21%的特异性.
- SHAP分析发现了诸如胸部紧张,ESR和舌头紫色等指数对糖尿病风险的显著非线性影响.
- 废弃试验证实了整体框架在单个算法方法上的优越性.
结论:
- 综合组合模型显著优于现有的糖尿病心血管并发症风险评估模型.
- 这种方法为早期查和个性化干预策略提供了可靠的工具.
- 该研究强调了整合TCM和西方医学诊断指数的临床价值.
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