可解释组合学习模型与夏普利的附加解释预测焦虑症状的风险在中国老年人与身体形状指数异常
Kai Wang1,2
1School of Medical Informatics and Engineering, Bengbu Medical College, Bengbu, China.
PloS one
|October 30, 2025
概括
这项研究开发了一种可解释的模型,使用A体形状指数 (ABSI) 预测中国老年人的焦虑症状. 关键预测因素包括婚姻状况,年龄和自我报告的健康状况,为早期焦虑识别提供了一个工具.
科学领域:
- 老年学是指老年学的学科.
- 精神病学是一个精神病学.
- 数据科学数据科学数据科学
背景情况:
- 焦虑症状在老年人中很普遍,特别是那些身体形状异常的人.
- 在预测健康结果方面,A体形状指数 (ABSI) 可能比身体质量指数 (BMI) 更好地衡量身体脂肪分布.
- 可解释的风险预测模型对于识别脆弱人群和指导干预至关重要.
研究的目的:
- 构建和验证一种可解释的风险预测模型,用于中国老年人与异常的身体形状的焦虑症状.
- 探索ABSI和焦虑症状之间的关联.
- 使用可解释的人工智能 (AI) 方法识别关键预测因素.
主要方法:
- 利用了2008-2014年中国长度健康长寿调查 (CLHLS) 的数据.
- 应用集体学习 (提升,堆叠,投票) 与数据平衡技术.
- 采用沙普利添加式解释 (SHAP) 来实现模型解释性和特征识别.
主要成果:
- ABSI显示与焦虑症状的积极关联 (P=0.038),男性的效果更强.
- 增强-ADASYN模型获得了高性能 (内部AUC=0.814,外部AUC=0.766-0.772).
- SHAP确定了婚姻状况,年龄,自我报告的健康状况,教育和幸福感作为重要的预测因素.
结论:
- 开发的模型提供了一个可解释的工具,用于在中国老年人中早期检测有异常身体形状的焦虑症状.
- ABSI是一个有价值的指标,可能优于BMI,用于评估与身体脂肪分布相关的风险.
- 研究结果支持有针对性的干预措施,将ABSI评估与心理社会支持策略相结合.
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