应用机器学习算法和SHAP解释来预测索马里生殖妇女的生育偏好
Jamilu Sani1, Salad Halane2, Abdiwali Mohamed Ahmed3
1Department of Demography and Social Statistics, Federal University Birnin Kebbi, Birnin Kebbi, Kebbi State, Nigeria.
Scientific reports
|July 20, 2025
概括
机器学习确定了影响索马里生育偏好的关键因素. 年龄,地区和最近的出生是最重要的,指导生殖健康干预措施.
科学领域:
- 生殖健康 生殖健康
- 人口统计学 人口统计学
- 机器学习应用 机器学习应用
背景情况:
- 生育偏好对人口动态和生殖健康至关重要,特别是在索马里等资源有限的环境中.
- 索马里高生育率和医疗保健不足需要了解生育决定因素才能进行有效的干预.
研究的目的:
- 利用机器学习 (ML) 和沙普利增量计算 (SHAP) 来确定索马里生育年龄妇女生育偏好的关键预测因素.
- 评估各种ML算法的预测性能,以了解生育偏好.
主要方法:
- 一项采用2020年索马里人口和健康调查 (SDHS) 数据的横截面研究,涉及8951名年龄在15-49岁之间的女性.
- 用七个ML算法预测的社会人口因素预测的二分化生育偏好 (想要更多的孩子与停止生育),随机森林被选为最佳.
- SHAP分析用于解释特征的重要性并量化对模型预测的贡献.
主要成果:
- 随机森林模型实现了高性能 (准确率:81%,AUROC:0.89).
- 由SHAP确定的关键预测因素包括年龄组,地区,过去五年出生人数,性别平等,婚姻状况,财富,教育,居住地和健康设施距离.
- 年龄较大的女性 (45-49岁) 和具有更高平价的女性更有可能不想要更多的孩子;更好的医疗设施与想要更多的孩子相关.
结论:
- ML和SHAP为了解低资源环境中的生育偏好提供了强大的工具.
- 确定了社会人口统计学决定因素,为索马里有针对性的生殖健康干预提供了可操作的见解.
- 未来的研究应该探索纵向数据和额外的心理社会因素,以提高模型的稳定性.
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