可解释的机器学习框架使用内脏脂肪指数来预测心综合征:一个调查加权的NHANES研究与SHAP解释
Sikai Xu1, Xiaoyun Sun1, Zhiyi Ouyang2
1Department of Medical Genetics, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Renal failure
|January 9, 2026
概括
较高的内脏脂肪指数 (VAI) 与患心综合征 (CRS) 的风险较高有关. 这项研究强调VAI是成年人CRS的关键预测因素,强调其在心脏代谢健康中的作用.
科学领域:
- 心脏病学 心脏病学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 代谢健康 代谢健康
背景情况:
- 心脏病综合征 (CRS) 描述了心脏和功能障碍之间的复杂相互作用.
- 内脏脂肪指数 (VAI) 量化了内脏脂肪和心脏代谢风险.
- VAI和CRS之间的关联在很大程度上仍未被探索.
研究的目的:
- 在一个庞大的,具有全国代表性的成年人群中调查VAI和CRS之间的关系.
- 确定VAI作为CRS风险的潜在生物标志物.
- 开发和验证机器学习模型,用于使用VAI.AI进行CRS预测.
主要方法:
- 分析国家健康和营养检查调查 (NHANES) 数据来自33,605名成年人的数据.
- 后勤回归和受限制的立方线 (RCS) 来评估VAI-CRS的关联.
- 开发和评估机器学习模型 (XGBoost,SVM,GLM) 与SHAP进行解释性.
主要成果:
- 较高的VAI独立地与CRS的几率增加有关 (OR = 1.29).
- 在VAI和CRS风险之间观察到线性关联.
- XGBoost模型将年龄,VAI和高血压确定为CRS的关键预测因素.
结论:
- 升高的VAI是心脏病综合征的独立风险因素.
- 在一般人群中,VAI 作为评估 CRS 风险的有价值指标.
- 这项研究提供了首个可解释的基于机器学习的CRS预测基准,其中包括VAI.
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