Go to AIMS 2025 Workshop T2P homepagePredicting Childhood Routine Immunization Status in Ethiopia Using Ensemble Machine Learning Algorithms
Keywords: Childhood Immunization, Ensemble Machine Learning, Vaccination, Predictive Model, Explainable AI
TL;DR: XGBoost accurately predicts childhood immunization status in Ethiopia, highlighting key maternal and child factors
Abstract: The study aims to develop a predictive model for assessing childhood immunization status in
Ethiopia using ensemble machine learning techniques.The research follows an experimental approach. Data from the Ethiopian Demographic and Health Survey (EDHS), collected at five-year
intervals, was preprocessed for quality assurance. The study employed several ensemble machine
learning algorithms, including Extreme Gradient Boosting (XGBoost), CatBoost, Random Forest (RF), and Gradient Boosting, with a One-Versus-Rest class decomposition method. A total
of 35,512 instances with 18 features were used, with an 80/20 training/testing dataset split. The
models were evaluated based on accuracy, with XGBoost achieving the highest performance
at 88.30%, followed by RF (87.17%), CatBoost (86.92%), and Gradient Boosting (84.16%).
SHAP (Shapley Additive Explanations) values were used to identify the most significant factors influencing immunization status, including child’s age, region, mother’s occupation, current
parity, and mother’s age.Using XGBoost and SHAP values extracted decision rules based on
feature importance. These rules reveal specific patterns related to immunization status, providing evidence-based insights for policymakers.XGBoost was selected as the best predictive model
for childhood immunization status in Ethiopia. The study highlights key factors for targeted
vaccination programs and scheduling, emphasizing the importance of early immunization and
maternal characteristics in improving child vaccination coverage.
Submission Number: 6
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