了解津巴布韦新生儿死亡率的驱动因素:使用调查数据的机器学习方法
Absolom Mbinda1, Rornald Muhumuza Kananura2, Arsene Brunelle Sandie2
1Department of Global, Public Health and Family Medicine, Faculty of Medicine and Health Sciences, University of Zimbabwe, Harare, Zimbabwe.
PLOS global public health
|January 29, 2026
概括
机器学习确定了津巴布韦新生儿死亡率的关键预测因素,新生儿死亡率很高. 干预措施应侧重于低出生体重,早期母乳养和产后护理,以减少死亡.
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:
- 津巴布韦的新生儿死亡率 (NMR) 超过了区域平均水平,并且正在增加,阻碍了实现可持续发展目标3的进展.
- 尽管其他儿童死亡率指标有所改善,但NMR仍然是津巴布韦面临的关键挑战.
研究的目的:
- 利用机器学习来识别津巴布韦新生儿死亡率的主要预测因素.
- 为减少新生儿死亡率的有针对性的干预措施提供信息.
主要方法:
- 汇集了三项津巴布韦人口健康调查 (2005-2015) 的二次数据分析,涉及16,941名活产婴儿.
- 开发和评估多个监督的二进制分类机器学习模型来预测新生儿死亡.
- 使用灵敏度和接收器运行曲线下的面积 (AUC ROC) 选择最佳模型,然后对变量重要性和影响评估进行后勤回归.
主要成果:
- 极端梯度提升模型是表现最好的模型,达到0.74.4的灵敏度.
- 确定的主要预测因素包括早期开始母乳养,出生体重,家庭规模和新生儿产后护理 (PNC).
- 低出生体重与新生儿死亡率有积极的关联,而较大的家庭规模,早期母乳养和PNC与此有负面关联.
结论:
- 机器学习有效地识别了影响津巴布韦新生儿死亡率的关键因素.
- 干预措施应优先考虑预防和管理低出生体重婴儿.
- 促进早期开始母乳养和确保新生儿产后护理对于改善新生儿存活率至关重要.
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