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TRUVACE RECORD VERSION
record: TRV-2026-1153
version: 3
kind: revised
reason: Editorial pass: sharper headline; gain/problem restated as concrete outcomes.
timestamp: 2026-11-04T09:50:24.310474Z
status: published
lens: g_space
sector: education
headline: Predicting zero-dose vaccination status in 27 sub-Saharan African countries: a machine learning approach
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: 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.

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). Our findings suggest that machine learning, particularly LightGBM and XGBoost, is moderately effective in predicting the risk of zero-dose vaccination among children.
limitation: 
tag: Evidence-backed gain
key_points: 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.
rundown: 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.
sources:
- peer_reviewed | BMJ Health & Care Informatics | https://doi.org/10.1136/bmjhci-2026-102188 | 2026-09-18
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