用SHAP增强的机器学习识别了青少年体重组中可修改的肥胖预测因素:2021年YRBSS分析
Yuhai Peng1, Zehan Xu2, Songjian Du3
1School of Physical Education, Henan University of Economics and Law, Henan, China.
PloS one
|October 10, 2025
概括
机器学习确定了青少年肥胖的主要预测因素:每日早餐,水果摄入量,体力活动,睡眠,屏幕时间和电子烟. 公共卫生战略应关注这些生活方式因素,以打击肥胖率的上升.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 青少年医学 青少年医学
背景情况:
- 青少年肥胖是一个全球公共卫生危机.
- 传统的方法往往无法捕捉到肥胖原因的复杂性.
- 这项研究使用先进的机器学习来确定青少年肥胖的主要驱动因素.
研究的目的:
- 使用机器学习识别和排名青少年肥胖的主要原因.
- 为开发有效的肥胖预防策略提供数据驱动的见解.
- 分析行为,饮食,睡眠和药物使用因素在青少年肥胖中的相互作用.
主要方法:
- 利用了2021年青年风险行为监测系统 (YRBSS) 对12-18岁青少年的数据.
- 应用随机森林和XGBoost机器学习模型来分析风险因素.
- 采用了夏普利添加式解释 (SHAP) 来提高模型的可解释性.
主要成果:
- 确定了关键预测因素:早餐频率,中度至强度体力活动 (MVPA) 日,睡眠时间,水果摄入量,屏幕时间和电子烟.
- 机器学习模型显示了适度的区分能力 (AUC~0.58).
- 较低的早餐频率,更少的MVPA日,更短的睡眠,更少的水果摄入量和增加的屏幕时间与更高的肥胖风险有关.
结论:
- 机器学习模型成功地确定了青少年肥胖的关键行为和生活方式预测因素.
- 公共卫生干预措施应强调促进每日早餐,水果消费,定期体力活动,充足的睡眠,减少屏幕时间,以及防止电子烟.
- 解决这些因素对于减轻青少年肥胖率的上升至关重要.
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