弥合数据差距:利用机器学习模型预测肯尼亚的亚国家母婴死亡率.
Hellen Muringi Mwaura1, Timothy Kelvin Kamanu2, Benard W Kulohoma3,4
1Department of Biochemistry, University of Nairobi, Nairobi, KEN.
Cureus
|November 27, 2024
概括
肯尼亚的孕产妇死亡率令人担忧,预测模型估计2022年每10万活产婴儿中有367例死亡. 这凸显了需要数据驱动干预来改善孕产妇健康结果的必要性.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 孕产妇死亡率仍然是一个关键的全球卫生挑战,特别是在撒哈拉以南非洲.
- 肯尼亚面临着不成比例的孕产妇死亡负担,危及国际卫生目标的实现.
- 有限可靠的孕产妇健康数据需要创新的预测建模.
研究的目的:
- 开发和应用一个预测模型来估计肯尼亚的孕产妇死亡率 (MMR).
- 使用机器学习技术分析MMR的次国家变异.
- 为基于证据的干预和产妇保健资源分配提供信息.
主要方法:
- 利用了来自撒哈拉以南非洲国家的人口和健康调查 (DHS) 数据.
- 在R中使用监督机器学习开发了多重线性回归模型.
- 应用该模型来预测肯尼亚的县级MMR,使用2022年KDHS数据.
主要成果:
- 发现了MMR和总生育率,母亲第一次分娩的年龄,产后诊所出院率,瘦身率和身体暴力之间的显著相关性.
- 该模型估计肯尼亚的国家MMR在2022年每10万活产婴儿中有367例死亡.
- 县级的MMR有很大的差异,从基西的49个到图尔卡纳的1794个.
结论:
- 肯尼亚的MMR在2022年与2019年的估计相比略有增加,可能与COVID-19有关.
- 预测建模提供了一种有价值的工具,可以补充对母亲健康的现有数据系统.
- 整合预测模型对于提高肯尼亚的孕产妇保健和资源分配至关重要.
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