应用机器学习来预测孟加拉国的质量ANC决定因素:一个BDHS-2022横截面研究
Tanzila Tamanna1, Shohel Mahmud2
1Department of Statistics and Data Science, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh.
Scientific reports
|October 28, 2025
概括
孟加拉国只有21.9%的妇女获得优质的产前护理 (ANC). 财富和教育等社会经济因素是关键决定因素,突出了改善孕产妇健康结果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
- 孕产妇健康 孕产妇健康
- 健康差异 在健康上的差异
背景情况:
- 优质的产前护理 (ANC) 对母亲和新生儿的福祉至关重要.
- 在孟加拉国,ANC获取和质量的显著差异仍然存在,特别是在服务不足的地区.
- 解决这些差距对于实现可持续发展目标 (SDG) 3至关重要.
研究的目的:
- 确定孟加拉国ANC质量的关键决定因素.
- 为数据驱动的孕产妇健康战略提供证据.
- 为旨在减少孕产妇死亡率的政策提供信息.
主要方法:
- 来自BDHS 2022的3549名女性 (15-49岁) 的数据分析.
- 应用机器学习模型 (随机森林,XGBM,神经网络,物流回归) 来预测质量的ANC.
- 使用夏普利添加式解释 (SHAP) 和基尼基数排名来评估特征重要性.
主要成果:
- 只有21.9%的女性获得了高质量的ANC.
- 确定最强的决定因素是财富指数,母亲和伴侣的教育,母亲的年龄,媒体曝光和城市居住.
- 随机森林模型实现了最高的预测性能 (准确率:74.1%).
- 财富指数被证实是最有影响力的预测指标.
结论:
- 在孟加拉国,ANC质量和覆盖率存在重大不平等.
- 解决社会经济和教育差异的有针对性的干预措施至关重要.
- 建议改善媒体宣传和城乡医疗保健接入,以加强孕产妇保健和实现可持续发展目标3.
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