BMJ health & care informatics · 2026
Predicting zero-dose vaccination status in 27 sub-Saharan African countries: a machine learning approach.
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Endehabtu Berhanu Fikadie, Addisu Taye Eliyas
Аннотация
OBJECTIVE: To develop a machine learning (ML)-based predictive model for assessing the risk of zero-dose children using Demographic Health Survey data in sub-Saharan Africa.
METHODS: This study analysed pooled Demographic and Health Survey data from 27 countries in sub-Saharan Africa collected between 2016 and 2024. Data preprocessing included imputation, balancing of unequal classes and systematic feature selection. Seven ML models were trained and evaluated using performance metrics such as accuracy, recall and F1-score. Feature importance was interpreted using SHapley Additive exPlanations (SHAP)-based analysis.
RESULTS: Among the seven models evaluated, LightGBM demonstrated the best overall performance, achieving the highest accuracy (80%), sensitivity (91%), and area under the receiver operating characteristic curve (AUROC = 0.78). SHAP analysis identified place of delivery, antenatal care attendance, maternal tetanus vaccination, household wealth and maternal education as top predictors.
DISCUSSION: Our findings suggest that machine learning, particularly LightGBM and XGBoost, is moderately effective in predicting the risk of zero-dose vaccination among children. This approach facilitates the easy identification of children most at risk of missing life-saving vaccinations.
CONCLUSION: The LightGBM model predict children at zero-dose risk, enabling early identification and supporting intervention strategies. Future studies will scale these models using larger, routine datasets for practical implementation.
METHODS: This study analysed pooled Demographic and Health Survey data from 27 countries in sub-Saharan Africa collected between 2016 and 2024. Data preprocessing included imputation, balancing of unequal classes and systematic feature selection. Seven ML models were trained and evaluated using performance metrics such as accuracy, recall and F1-score. Feature importance was interpreted using SHapley Additive exPlanations (SHAP)-based analysis.
RESULTS: Among the seven models evaluated, LightGBM demonstrated the best overall performance, achieving the highest accuracy (80%), sensitivity (91%), and area under the receiver operating characteristic curve (AUROC = 0.78). SHAP analysis identified place of delivery, antenatal care attendance, maternal tetanus vaccination, household wealth and maternal education as top predictors.
DISCUSSION: Our findings suggest that machine learning, particularly LightGBM and XGBoost, is moderately effective in predicting the risk of zero-dose vaccination among children. This approach facilitates the easy identification of children most at risk of missing life-saving vaccinations.
CONCLUSION: The LightGBM model predict children at zero-dose risk, enabling early identification and supporting intervention strategies. Future studies will scale these models using larger, routine datasets for practical implementation.