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TRUVACE RECORD VERSION
record: TRV-2026-1153
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-20T06:54:08.830758Z
status: published
lens: p_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: (none)
problem_title: 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.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: 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.
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 problem
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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