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下斯列西亚健康捐赠者队列的风险分析:统计见解和机器学习分类

Przemysław Wieczorek¹, Magdalena Krupińska¹, Patrycja Gazinska²

¹Screening of Biological Activity Assays and Collection of Biological Material Laboratory, Wroclaw Medical University Biobank, 50-556 Wroclaw, Poland.

Journal of clinical medicine

|December 30, 2025

概括

机器学习模型通过识别主要的代谢预测因素,如葡萄糖和甘油三等,显著改善了代谢综合征 (MetS) 的预测. 这种方法增强了针对性预防策略的风险分层.

科学领域:

代谢综合征研究代谢综合征研究
生物统计学生物统计学
医疗保健中的机器学习

背景情况:

代谢综合征 (MetS) 是2型糖尿病和心血管疾病的重要危险因素.
识别关键的代谢预测因素对于早期干预至关重要.

研究的目的:

在中欧队列中确定MetS的关键代谢预测因子.
将经典统计模型的预测性能与现代机器学习 (ML) 模型进行比较.

主要方法:

分析了956名来自下斯列西亚健康捐赠者队列的成年人.
使用多变量后勤回归和先进的ML模型 (随机森林,XGBoost,LightGBM,CatBoost).
使用精度,F1-宏,ROC AUC和PR AUC评估模型性能,并使用SHAP值进行解释.

主要成果:

超重/肥胖个体表现出更高的禁食葡萄糖,胰岛素和静脉血压,以及更低的HDL胆固醇.
经典后勤回归确定腰围,BMI,甘油三,高血糖,禁食葡萄糖和静脉血压作为独立预测因素 (ROC AUC 0.98).
ML模型,特别是CatBoost,XGBoost和LightGBM,实现了优异的歧视 (ROC AUC ≥ 0.99,PR AUC ≥ 0.98),SHAP证实了关键预测因素.

结论:

将经典回归与梯度增强ML模型相结合,可以大大提高MetS风险识别.
XGBoost,LightGBM和CatBoost为MetS风险分层提供了近乎完美的,可解释的歧视.
该框架通过识别有风险的个体,包括"亚临床"概率区域来支持有针对性的预防策略.

关键词:

在 CatBoost 中使用 CatBoost.在HDL胆固醇.波兰波兰波兰波兰这就是 SHAP SHAP 的意思.机器学习是机器学习.代谢综合征代谢综合征人口队列人口队列.风险分层的风险分层.压缩性血压压缩性血压是一种血压.三甲酸甘油三甲酸甘油三甲酸甘油

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