采用先进的监督机器学习方法来预测埃塞俄比亚6-23个月龄的儿童中微量营养素摄入状况
Alemu Birara Zemariam1, Molalign Aligaz Adisu1, Aklilu Abera Habesse1
1Department of Pediatrics and Child Health Nursing, School of Nursing, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia.
Frontiers in nutrition
|June 26, 2024
概括
微量营养素缺乏影响埃塞俄比亚儿童,但机器学习模型可以准确预测摄入状态. 地区和母亲教育等关键因素影响微量营养素的消费,指导有针对性的干预措施.
科学领域:
- 儿童营养学 儿童营养学
- 公共卫生 公共卫生
- 计算生物学 计算生物学
背景情况:
- 微量营养素缺乏对儿童的生长和发育构成重大公共卫生挑战,特别是在埃塞俄比亚.
- 关于使用先进的统计方法 (如机器学习) 预测微量营养素摄入量的经验证据有限.
研究的目的:
- 采用先进的监督机器学习算法来预测6-23个月龄的埃塞俄比亚儿童的微量营养素摄入状态.
- 为了确定关键的预测因素和关联与微量营养素摄入量在这个人群.
主要方法:
- 利用2016年埃塞俄比亚人口和健康调查的2,499名儿童 (6-23个月) 的数据.
- 应用了12个机器学习算法,特征选择 (Boruta),数据平衡和超参数调整.
- 使用先验算法进行关联规则挖掘,以确定与微量营养素摄入相关的常见模式.
主要成果:
- 在12-23个月的儿童中,69.15%的儿童摄入了足够的微量营养素.
- 随机森林,Catboost和光梯度提升模型显示出优异的预测性能.
- 关键预测因素包括地区,财富指数,分娩地点,母亲的职业,孩子的年龄,父亲的教育,想要更多的孩子,媒体曝光,宗教,居住和产前护理 (ANC) 随访.
结论:
- 高性能机器学习模型有效预测微量营养素摄入状态,并确定关键的相关因素.
- 这些发现支持政策制定者和医疗保健提供者制定有针对性的干预措施,以改善微量营养素补充剂的吸收.
- 利用已识别的关联规则可以提高儿童健康结果,并减轻埃塞俄比亚微量营养素缺乏的影响.
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