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TRV-2026-1153Certified recordPeer-reviewed

Predicting 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…

Education · P Space — documented harm · certified 2026-09-20 · v1 · article view · machine-readable

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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.

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Truvace Impact Record TRV-2026-1153, v1: “Predicting zero-dose vaccination status in 27 sub-Saharan African countries: a machine learning approach.” Truvace, 2026-09-20. /record/TRV-2026-1153 (accessed at citation time). sha256 967f3d422bf8d2b2…

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