机器学习预测使用身体组成的美国成年人代谢功能障碍相关的脂肪肝疾病风险:基于夏普利添加剂的可解释分析
Yan Hong1, Xinrong Chen2, Ling Wang3
1Affiliated Guangdong Hospital of Integrated Traditional Chinese and Western Medicine of Guangzhou University of Chinese Medicine, Guangzhou University of Chinese Medicine, Foshan, China.
Frontiers in nutrition
|July 15, 2025
概括
机器学习模型使用身体组成准确地预测代谢功能障碍相关的脂肪肝疾病 (MAFLD). 内脏脂肪组织成为一个关键预测因素,突出了脂肪分布在MAFLD风险中的重要性.
科学领域:
- 医疗信息学 医疗信息学
- 肝病学 肝病学是一种肝病学.
- 机器学习 机器学习
背景情况:
- 与代谢功能障碍相关的脂肪肝疾病 (MAFLD) 是与肥胖和代谢问题相关的日益严重的全球健康问题.
- 由于脂肪分布评估不佳,目前的人类测量指标,如BMI在评估MAFLD风险方面存在局限性.
- 这项研究解决了使用先进的身体成分分析改进MAFLD预测的需要.
研究的目的:
- 开发和验证用于预测MAFLD风险的机器学习 (ML) 模型.
- 利用详细的身体组成指标来提高MAFLD预测.
- 利用可解释的人工智能探索特定脂肪组织储存在MAFLD病变发生中的作用.
主要方法:
- 利用了2017-2018年国家健康和营养检查调查 (NHANES) 的数据.
- 实施并比较了6个ML算法 (DT,SVM,GLM,GBM,RF,XGBoost) 用于MAFLD预测.
- 雇佣了Boruta用于特征选择和SHAP用于ML模型的解释性.
主要成果:
- 梯度提升机 (GBM) 算法在验证集上表现出优异的性能,AUC为0.879.
- SHAP分析发现内脏脂肪组织 (VAT) 是MAFLD最重要的预测因子.
- 身体质量指数 (BMI) 和皮下脂肪组织 (SAT) 也是MAFLD预测的关键因素.
结论:
- 整合身体组成数据的机器学习模型为MAFLD风险预测提供了强大的方法.
- GBM模型提供了高准确性和可解释性,对临床决策支持和公共卫生非常有价值.
- 肥胖度量,特别是增值税,对于理解和预测MAFLD至关重要,超过了传统的肥胖指数.
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