可解释机器学习用于慢性病预测:来自SHAP和LIME分析的见解
El Mehdi Chouit1, Mohamed Rachdi2, Mostafa Bellafkih1
1RAISS Laboratory, Department of Mathematics and Computer Science, National Institute of Posts and Telecommunications (INPT), Rabat, Morocco.
PloS one
|February 26, 2026
概括
本研究引入了一种可解释的机器学习模型,用于早期检测慢性病 (CKD). 该框架提高了使用XGBoost与SHAP和LIME预测CKD的透明度和可靠性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 腎臟醫學 腎臟醫學
背景情况:
- 慢性病 (CKD) 需要早期检测,以改善患者的治疗结果.
- 在医疗保健中采用机器学习 (ML) 面临着透明度挑战.
- 可解释AI对于临床整合和信任至关重要.
研究的目的:
- 开发和评估可解释的机器学习框架,用于透明的CKD预测.
- 使用可解释性技术评估已识别的预测因子的临床相关性.
- 通过各种数据集来证明框架的可靠性.
主要方法:
- 使用XGBoost开发了一个可解释的ML框架,使用SMOTE进行了优化.
- 集成的夏普利添加式解释 (SHAP) 为全球的可解释性.
- 使用本地可解释的模型不可知解释 (LIME) 来获得本地,患者层面的洞察力.
- 在两个不同的数据集上验证了模型:阿联塔瓦姆医院和UCI CKD数据.
主要成果:
- 实现了高精度:88.4% (AUC=0.904) 在医院数据上和94.6% (AUC=0.948) 在UCI数据上.
- 确定了关键预测因素:eGFR基线,HbA1c,胆固醇基线 (医院);特异性重量,血红蛋白,血清肌素 (UCI).
- SHAP和LIME分析显示了趋同,证实了模型的可靠性和临床相关性.
结论:
- 可解释的ML框架提高了CKD预测的透明度.
- 该方法确保了临床现实的性能,并解决了在医疗保健中采用人工智能的障碍.
- 为整合可解释AI到CKD查和管理工作流程提供了基础.
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