探索埃塞俄比亚社区医疗保险入学的机器学习分类
Seyifemickael Amare Yilema1,2, Yegnanew A Shiferaw3, Yikeber Abebaw Moyehodie1
1Department of Statistics, Debre Tabor University, Debre Tabor, Ethiopia.
Frontiers in public health
|August 4, 2025
概括
随机森林机器学习模型准确地预测了埃塞俄比亚的社区医疗保险 (CBHI) 注册情况. 关键预测因素包括年龄,财富,家庭规模和土地使用,为更好的UHC提供政策信息.
科学领域:
- 卫生经济学 卫生经济学
- 机器学习在公共卫生中的应用
- 全民健康覆盖 (UHC) 是指全民健康覆盖.
背景情况:
- 基于社区的医疗保险 (CBHI) 对于实现全民健康覆盖 (UHC) 和减轻撒哈拉以南非洲疾病的财务负担至关重要.
- 埃塞俄比亚和其他低收入和中等收入国家一样,越来越多地采用CBHI计划.
- 在最大限度地提高UHC和保护公民免受与医疗保健相关的财务困境方面,仍然存在挑战.
研究的目的:
- 确定最准确的机器学习 (ML) 算法来预测埃塞俄比亚的CBHI入学率.
- 确定影响CBHI招生的主要人口和社会经济因素.
主要方法:
- 利用了2019年埃塞俄比亚小型人口和健康调查 (EMDHS) 的数据.
- 评估了七个ML模型:LDA,SVM,KNN,CART和RF,使用ROC曲线和其他指标评估预测准确性.
主要成果:
- 与其他模型相比,随机森林 (RF) 算法表现出优越的预测性能.
- 确定CBHI入学的重要预测因素是年龄,财富指数,家庭规模和土地利用.
结论:
- 射频模型提供了一个非常准确的方法来分类埃塞俄比亚的CBHI招生.
- 了解年龄,财富,家庭规模和土地使用的影响,可以指导有针对性的干预措施,以促进CBHI的采用.
- 调查结果支持政策制定者和卫生专业人员制定有效的战略,以提高CBHI入学率并推进UHC目标.
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