优化机器学习模型,以预测埃塞俄比亚农村孕妇的医疗服务获取和决定因素
Abdulaziz Kebede Kassaw1, Atitegeb Abera Kidie2, Natnael Amare Tesfa3
1Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
Scientific reports
|November 18, 2025
概括
机器学习,特别是渐变增强,有效地预测埃塞俄比亚农村孕妇获得医疗保健. 更高的财富和媒体曝光度提高了获取机会,而缺乏教育仍然是一个重要的障碍.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 机器学习应用 机器学习应用
背景情况:
- 埃塞俄比亚农村的孕妇面临着严重的医疗保健接入障碍,影响着孕产妇和新生儿的结果.
- 传统的统计与影响医疗保健获取的复杂因素作斗争.
- 机器学习 (ML) 为分析大型数据集和预测医疗服务获取提供了先进的工具.
研究的目的:
- 确定最有效的机器学习算法,用于预测埃塞俄比亚农村孕妇获得医疗保健服务的机会.
- 分析影响这一群体获得医疗保健的决定因素.
主要方法:
- 利用了来自埃塞俄比亚人口和健康调查 (EDHS) 的数据.
- 应用了七个监督的ML分类器:梯度提升,随机森林,KNN,决策树,SVM,后勤回归和天真贝斯.
- 使用准确度和接收器操作特征曲线 (AUC) 下的面积评估模型性能;用于特征解释使用SHAP.
主要成果:
- 梯度提升实现了最高的预测性能,AUC为81.40%和准确率为79.55%.
- 获得医疗保健的保护因素包括较高的家庭财富,阿姆哈拉地区的居住地,媒体曝光和避免饮酒.
- 缺乏正规教育被认为是获得产妇健康服务的主要障碍.
结论:
- 渐变增强模型在预测埃塞俄比亚农村孕妇获得医疗保健方面表现出卓越的有效性.
- 社会经济地位,区域因素,媒体曝光和行为选择显著影响医疗服务获取.
- 结果支持使用ML用于数据驱动的政策和有针对性的干预措施,以改善资源有限的环境中的孕产妇健康.
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