基于身体活动和饮食习惯的肥胖程度的预测,使用机器学习模型与可解释的人工智能集成
Yasin Görmez1, Fatma Hilal Yagin2, Burak Yagin3
1Department of Management Information Systems, Faculty of Economics and Administrative Sciences, Sivas Cumhuriyet University, Sivas, Türkiye.
Frontiers in physiology
|July 31, 2025
概括
机器学习模型通过分析身体活动和饮食,准确地预测肥胖程度. 可解释的人工智能方法揭示了年龄,体重和饮食模式等关键风险因素,使得有针对性的干预措施成为可能.
科学领域:
- 计算式健康科学 计算式健康科学
- 生物医学信息学是生物医学信息学.
- 医疗保健中的人工智能
背景情况:
- 肥胖是一个复杂的健康问题,受到生活方式因素的影响.
- 预测模型可以帮助识别有风险的个体.
- 了解风险因素对于有效的干预策略至关重要.
研究的目的:
- 开发和评估用于预测肥胖程度的机器学习 (ML) 模型.
- 整合可解释的人工智能 (XAI) 以透明地识别风险因素.
- 提高对生活方式对肥胖的影响的理解.
主要方法:
- 训练和评估了六种ML模型:伯努利的天真贝叶斯,CatBoost,决策树,额外的树木分类器,基于直方图的梯度增强和支持向量机器.
- 使用随机搜索进行了超参数调整,并通过重复的持久测试评估了模型性能.
- 为了实现本地和全球特征的解释性,使用了SHAP (夏普利附加注释) 和LIME (局部可解释的模型独立解释).
主要成果:
- CatBoost模型在准确性,精度,F1得分和AUC指标方面表现出卓越的表现.
- 年龄,体重,身高和特定的饮食模式被确定为肥胖的重要预测因素.
- 在解释性方面,LIME提供了更高的保真度,而SHAP则在模型中提供了更好的稀疏性和一致性.
结论:
- 集成的ML和XAI模型准确预测肥胖和阐明有助于风险因素.
- 像SHAP和LIME这样的XAI技术提高了模型透明度,有助于识别与生活方式相关的肥胖风险.
- 这些发现支持开发精确的,数据驱动的肥胖管理干预策略.
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