模拟埃塞俄比亚的零剂量儿童:对模型性能和预测变量进行机器学习视角
Berhanu Fikadie Endehabtu1,2, Kassahun Alemu2,3, Shegaw Anagaw Mengiste4
1Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, 196, Ethiopia, 251 921013129.
JMIR pediatrics and parenting
|February 2, 2026
概括
机器学习模型有效地识别了埃塞俄比亚儿童在零剂量 (ZD) 的高风险,使得有针对性的干预措施,以提高疫苗接种覆盖率. 关键预测因素包括获得护理和使用产妇健康服务.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 流行病学 流行病学
背景情况:
- 在低收入和中等收入国家,儿童疫苗接种仍然是一个挑战,许多儿童没有接种疫苗.
- "2030年疫苗接种议程"旨在通过识别风险人群来减少未接种疫苗的儿童.
- 识别和接触未接种疫苗的儿童的有效策略有限.
研究的目的:
- 开发一个机器学习 (ML) 模型来预测埃塞俄比亚的零剂量 (ZD) 儿童.
- 确定与埃塞俄比亚儿童中ZD状态相关的最有影响力的预测因素.
主要方法:
- 监督的ML算法被应用到13666名12-35个月龄的儿童的全国免疫评估调查数据.
- 用准确度,AUC,精度,回忆和F1分数来评估模型性能.
- 沙普利添加式分析被用来确定预测变量的重要性.
主要成果:
- 光梯度增强机 (LGBM) 显示出卓越的性能,准确率为93%,AUC为97%,精度为94%,回忆率为91%.
- 黄金病状况的重要预测因素包括对疫苗接种益处的不良感知,缺乏产前护理,以及距离免疫服务的距离.
- 其他关键预测因素是母亲没有接种毒素疫苗,获得医疗保健的机会有限.
结论:
- 机器学习模型,特别是LGBM,可以有效地预测患有ZD风险的儿童,指导有针对性的公共卫生干预.
- 解决诸如免疫接种,产妇护理利用率和对疫苗接种益处的认识等预测因素至关重要.
- 专注于加强孕产妇护理,护理人员教育和改善免疫接种能力的干预措施可以减少ZD患病率和疫苗接种不平等.
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