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通过可解释的机器学习模型解读肌肉质量下降的环境化学基础
Zhen Feng1, Ying'ao Chen2, Yuxin Guo3
1Joint Centre of Translational Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China; Joint Centre of Translational Medicine, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, People's Republic of China; College of Information and Engineering, Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.
环境化学物质 (ECs) 与与年龄相关的肌肉衰退 (肉症) 有关. 机器学习模型识别了特定的EC,如3-基酸和,可以预测肉症,提供了对内分泌相关疾病的见解.
科学领域:
- 环境健康 环境健康
- 老年学是一门学科.
- 毒理学 毒理学 毒理学
背景情况:
- 肌肉质量和功能与年龄相关的下降,与包括内分泌疾病在内的各种健康问题有关.
- 环境化学品 (ECs) 是工业副产品,可能导致肌肉质量恶化.
研究的目的:
- 调查肌肉质量下降和暴露于各种ECs之间的关联.
- 利用机器学习模型分析2011-2014年全国健康和营养检查调查 (NHANES) 周期的数据.
主要方法:
- 训练了六个机器学习模型,以根据EC和非EC暴露来区分低和正常的肌肉质量指数状态.
- 采用沙普利增量解释 (SHAP) 方法来解释模型的可解释性.
- 评估了各种机器学习性能指标.
主要成果:
- 随机森林 (RF) 模型在预测肌肉质量状态方面表现出卓越的表现.
- 发现EC可以独立预测肌肉质量下降,准确度很高 (AUC = 0.793,AUPRC = 0.808).
- SHAP分析确定了特定的EC,包括尿液中的3-基酸和,与肌肉质量下降有显著的关联.
结论:
- EC可以有效地预测肌肉质量下降,RF模型的性能证明了这一点.
- SHAP识别的EC与肌肉质量下降和肉症密切相关.
- 这些发现提供了有价值的洞察力,可以了解美国人口中可能导致肉症和相关内分泌疾病的EC.
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