在中国老年人中使用饮食行为和机器学习算法预测失脂症
Biying Wang1, Luotao Lin2, Wenjun Wang3
1School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Three Gorges University Hospital of Traditional Chinese Medicine & Yichang Hospital of Traditional Chinese Medicine, Yichang, Hubei, China.
Public health
|December 20, 2024
概括
机器学习准确地预测了中国老年人的失脂症风险. 关键预测因素包括坚果消费,儿童水源和住房类型,提供新的公共卫生策略.
科学领域:
- 老年学是一门学科.
- 公共卫生 公共卫生
- 计算医学是一种计算医学.
背景情况:
- 脱脂症对老年人群构成重大健康风险.
- 预测中国老年人失脂症的预测模型对于公共卫生干预至关重要.
- 整合生活方式因素,如饮食,可以改善风险预测.
研究的目的:
- 用机器学习 (ML) 和饮食分析来预测老年中国成年人的失脂症风险.
- 在这个人口群体中确定失脂症的新型预测因子.
- 为预防和控制失脂症的公共卫生策略提供信息.
主要方法:
- 来自2018年中国纵向健康长寿调查的13,668名65岁以上的中国成年人的横截面研究.
- 使用各种ML算法 (光梯度增强机,支持向量机,XGBoost等) 和逻辑回归用于失脂症预测.
- 分析了饮食行为和其他生活方式因素作为潜在的预测因素.
主要成果:
- 在研究参与者中,脱脂症的患病率为5.4%.
- 光梯度增强机 (LGBM) 显示出最高的预测准确性 (AUC > 0.70),优于其他ML模型和后勤回归.
- 坚果消费的频率,儿童的水源和住房类型都被认为是脱脂症的重要预测因素.
结论:
- 结合饮食习惯的ML模型有效地预测了中国老年人的脂质障碍症.
- 确定了新的风险因素,包括坚果消费频率,儿童水源和住房类型.
- 这些发现为开发新的策略提供了潜在的目标,以预防和管理老年人中的失脂症.
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