基于机器学习的算法来识别索马里6-23个月龄的儿童中不充分的饮食频率相关的因素:索马里人口和健康调查2020年证据
Mohamed Abdirahim Omar1,2, Omran Salih3
1School of Postgraduate Studies and Research, Amoud University, Borama, Somalia.
Digital health
|February 2, 2026
概括
不充分的饮食频率影响了索马里78.51%的儿童. 出生顺序是关键因素,突出了打击儿童营养不良的有针对性的干预措施的必要性.
科学领域:
- 公共卫生 公共卫生
- 儿童营养学 儿童营养学
- 机器学习应用 机器学习应用
背景情况:
- 不充分的餐饮频率 (IMF) 是索马里的一个重大公共卫生挑战,影响儿童营养和阻碍可持续发展目标.
- 在6-23个月龄的儿童中,营养不良的高率迫切需要对导致的因素进行调查.
研究的目的:
- 确定与索马里6-23个月龄的儿童不充分的饮食频率相关的最有影响力的因素.
- 为旨在减少营养不良的有针对性的公共卫生干预提供信息.
主要方法:
- 利用了2020年索马里人口和健康调查中的4066名儿童的数据.
- 采用了五种机器学习算法 (逻辑回归,决策树,随机森林,支持矢量机,梯度提升) 来预测IMF.
- 使用精度和AUC-ROC指标评估模型性能,并分析特征的重要性.
主要成果:
- 国际货币基金组织的流行率惊人地高达78.51%.
- 梯度增强模型实现了最高的性能 (准确率:89.55%,AUC-ROC:92.77%).
- 出生顺序是最重要的预测因素 (74.07%的基尼重要性),其次是儿童年龄,母乳养状况,母亲教育,家庭财富和地区.
结论:
- 国际货币基金组织的高普及率强调了需要立即,有针对性的干预.
- 干预策略应考虑诸如出生顺序,母亲教育和减少贫困等因素.
- 机器学习模型显示,在资源有限的环境中,预测性查和告知公共卫生战略具有前途.
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