预测麻疹的机器学习算法在埃塞俄比亚的12-23个月儿童中出现了一次疫苗接种中断
1Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Amhara, Ethiopia merryalem101@gmail.com.
BMJ open
|November 14, 2024
概括
埃塞俄比亚的麻疹疫苗接种中断率很高,超过10%. 关键预测因素包括较年轻的孕产妇年龄,较低的教育程度,失业率和特定的地区居住,需要有针对性的干预措施来改善覆盖率.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗保健中的机器学习
背景情况:
- 埃塞俄比亚面临着反复爆发的麻疹疫情,从2021年到2023年,病例增加了五倍.
- 没有接种疫苗的状态是麻疹复苏的主要驱动因素,正如世卫组织所指出的那样.
- 麻疹疫苗接种中断率很高,需要了解有助于疫苗接种的因素.
研究的目的:
- 预测与埃塞俄比亚麻疹疫苗接种中断相关的关键因素.
- 应用和评估八个监督机器学习算法,用于中断预测.
- 使用Shapley值来解释模型预测和识别影响因素.
主要方法:
- 利用了2016年埃塞俄比亚人口与健康调查数据.
- 实现并比较了8个机器学习算法,包括XGBoost,随机森林和物流回归.
- 使用准确度,精度,回忆,F1得分和AUC评估模型性能;使用Shapley值来解释特征.
主要成果:
- 在预测麻疹疫苗接种弃率方面,XGBoost表现优异 (准确率:73.9%,AUC:0.813).
- 退学的重要预测因素包括母亲年龄较小,特定的宗教信仰,母亲教育水平较低,母亲失业率,居住在奥罗米亚和索马里地区,家庭规模较大和父亲年龄较大.
- 沙普利值为表现最好的XGBoost模型提供了个体特征的重要性.
结论:
- 埃塞俄比亚的麻疹疫苗接种中断率超过了<10%的推值.
- 有针对性的干预,包括公众宣传活动和与宗教机构和卫生工作者建立伙伴关系至关重要.
- 将干预措施集中在特定的贫困地区,可以帮助降低退学率,提高麻疹疫苗覆盖率.
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