集体学习预测埃塞俄比亚生育年龄妇女的短分娩间隔:来自EDHS 2016-2019年的证据
Jenberu Mekurianew Kelkay1, Deje Sendek Anteneh2, Henok Dessie Wubneh2
1Department of Health Informatics, College of Health Sciences, Debark University, Debark, Ethiopia. jenberu.uog11@gmail.com.
BMC pregnancy and childbirth
|February 5, 2025
概括
在埃塞俄比亚,短的分娩间隔有助于孕产妇死亡. 合体学习准确地预测了短的分娩间隔,确定了区域和教育等关键因素,以告知干预.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 人口统计学 人口统计学
背景情况:
- 短分娩间隔 (<33个月) 是埃塞俄比亚孕产妇死亡的主要原因,约有822例每天死亡.
- 在低收入国家,确定短分娩间隔的预测因素对于母亲健康干预至关重要.
研究的目的:
- 预测埃塞俄比亚生育年龄妇女的短分娩间隔和相关因素.
- 评估集合学习算法的有效性,以预测短的分娩间隔.
主要方法:
- 2016-2019年埃塞俄比亚人口和健康调查的二次数据分析 (n=12,573).
- 在 Jupyter Notebook 中使用 Python 实现的集成机器学习分类模型.
- 数据使用 Stata 17 处理,并使用 imblearn, sklearn 和 xgboost 包进行分析.
主要成果:
- 随机森林实现了最高的性能,准确率为97.84%,回忆率为99.70%,精度为98.95%.
- 短分娩间隔的关键预测因素包括地区,居住地,妇女年龄,孩子的性别,教育,到医疗机构的距离,丈夫的教育和宗教.
- 合体学习模型显示出对短出生间隔状态的高预测能力.
结论:
- 随机森林是一个非常有效的预测模型,用于在埃塞俄比亚的背景下短的分娩间隔.
- 合体学习算法为孕产妇健康计划的决策支持提供了有价值的工具.
- 了解主要的人口统计和社会经济因素对于减少分娩间隔短和改善母亲的结果至关重要.
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