Predicting 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, demographic, and socioeconomic factors. Similarly, there is limited research regarding the use of interpretable deep learning and advanced ensemble methods to improve model performance in predicting RI defaulters and establishing significant predictors at the national level in the LMIC context, like Uganda.