一个堆叠的整体机器学习模型用于预测东非五价值3疫苗接种中断

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Meron Asmamaw Alemayehu¹, Shimels Derso Kebede², Agmasie Damtew Walle³

¹Department of Epidemiology and Biostatistics, College of Medicine and Health Sciences, Institute of Public Health, University of Gondar, Gondar, Ethiopia.

Frontiers in big data

|April 22, 2025

概括

预测东非儿童疫苗接种中断至关重要. 一个机器学习模型确定了分娩地点和母亲收入等因素,使得有针对性的干预措施能够提高Penta 3疫苗完成率.

科学领域:

公共卫生公共卫生
机器学习机器学习
疫苗学疫苗学疫苗学

背景情况:

东非的儿童死亡率仍然很高,五价疫苗 (Penta 3) 的第三剂量完成率不足.
预测建模可以确定有针对性的干预措施的风险人群.

研究的目的:

用堆叠组合机器学习模型来预测Penta 3疫苗接种中断.
为了确定Penta 3在东非中断的关键预测因素.
为改善疫苗接种完成率的战略提供信息.

主要方法:

使用7个基本机器学习算法和3个meta-learners (Random Forest,GLM,XGBoost) 开发了一个堆叠组合模型.
使用LASSO和Boruta算法进行了特征选择.
用准确度和AUC来评估模型性能,并使用SHAP值进行解释.

主要成果:

具有网格搜索优化和LASSO功能选择的XGBoost元学习器实现了93.9%的准确性和99.4%的AUC.
3产的主要预测因素包括分娩地点,母亲的决策自主性,母亲的收入和获得医疗保健.
家庭分娩增加了退学风险,而产后护理和医疗保险降低了这一风险.

结论:

机器学习有效地预测了东非的Penta 3疫苗放弃.
干预措施应侧重于改善孕产妇的生计,获得医疗保健,并促进机构分娩.
有针对性的策略可以提高Penta 3疫苗的完成率,并减少儿童死亡率.

关键词:

东非非洲东非接种Penta 3疫苗的人群在SHAP中,价值是SHAP值.机器学习是机器学习.堆叠的组合模型模型

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