acePredicting zero-dose vaccination status in 27 sub-Saharan African countries: a machine learning approach
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. 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…
Tested on survey data from 27 sub-Saharan African countries, the best AI model (LightGBM) predicted which children were at risk of getting no vaccines with 80% accuracy and 91% sensitivity.
Evidence
- Peer-reviewedBMJ Health & Care Informatics2026-09-18
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Truvace Impact Record TRV-2026-1153, v3: “Predicting zero-dose vaccination status in 27 sub-Saharan African countries: a machine learning approach.” Truvace, 2026-11-04. /record/TRV-2026-1153 (accessed at citation time). sha256 02d3cd74e02d8640…
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