acePredicting Measles Vaccination Defaulting in Uganda and Kenya: Comparative Evaluation of Custom Soft Voting and TabNet Stacked Ensemble Architectures
Background Measles is a major cause of death among infants despite free routine immunization (RI) in low- and middle-income countries (LMICs). Vaccination defaulting contributes to measles outbreak occurrences. Conventional immunization defaulter identification methods are expensive to implement, prone to data falsification, reactive, and overestimate uptake. Several machine learning studies leveraged the use of child and maternal characteristics as predictors of immunization defaulting, overlooking community, d…
Ensemble models combining TabNet with tree-based classifiers identified likely measles vaccine defaulters better than individual models, and SHAP pinpointed country-specific drivers that could guide targeted outreach.
In Uganda, the best hybrid ensemble's performance gain was statistically insignificant, so the improvement over simpler models there is not firmly established.
Evidence
- Peer-reviewedJMIR AI2026-10-13
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Truvace Impact Record TRV-2026-2050, v1: “Predicting Measles Vaccination Defaulting in Uganda and Kenya: Comparative Evaluation of Custom Soft Voting and TabNet Stacked Ensemble Architectures.” Truvace, 2026-10-20. /record/TRV-2026-2050 (accessed at citation time). sha256 d1e25da5fe622f20…
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