使用机器学习算法识别孟加拉国妇女赋权的有影响力的决定因素
Md A Salam1, Samiul Islam1, Md Mahfuz Uddin1
1Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh.
PloS one
|December 9, 2025
概括
机器学习确定了影响女性的关键因素.
科学领域:
- 使用先进的机器学习 (ML) 进行社会经济研究.
- 专注于发展中国家妇女赋权的决定因素.
背景情况:
- 妇女赋权对于中低收入国家的发展至关重要,特别是孟加拉国.
- 了解这些决定因素是实现国家发展目标和可持续发展目标5 (SDG-5) 的关键.
研究的目的:
- 确定孟加拉国妇女赋权的有影响力的决定因素.
- 开发和验证用于女性赋权地位的预测机器学习模型.
主要方法:
- 孟加拉国人口和健康调查 (BDHS) 2022年的就业数据 (n=18,600名15-49岁的女性).
- 利用后勤回归和Boruta特征选择来识别关键变量.
- 应用并比较了八种机器学习算法 (包括随机森林,XGBoost,ANN) 用于预测和SHAP分析用于确定因素的识别.
主要成果:
- 随机森林 (RF) 模型实现了最高的性能 (精度:71.07%,F1得分:81.58%,AUC:0.676).
- 确定的主要决定因素包括年龄,划分,财富指数,工作状态,家庭规模,丈夫的教育和受访者的教育.
结论:
- 该研究提出了一种最佳的机器学习模型,用于预测孟加拉国妇女赋权.
- 确定了重要的社会经济和人口因素,为政策干预提供了可操作的见解.
- 调查结果支持有针对性的战略,以推进妇女赋权,并到2030年实现可持续发展目标5.
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