## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Season** & **Month Range** & **Decision Rule** \\ \hline Major Rains & [April, July] & 1 if birth month \(\in\) [April, July], else 0 \\ Minor Rains & [September, October] & 1 if birth month \(\in\) [September, October], else 0 \\ Dry & [November, March] & 1 if birth month \(\in\) [November, March], else 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification scheme for birth seasonality features. \begin{table}
\begin{tabular}{c c} \hline \hline
**Min Age** & **NON-DEFAULT = 1 If** \\ \hline Birth & BCG = 1 and OPV0 = 1 \\
6 weeks & NON-DEFAULT = 1 at birth and OPV1 = 1 and PENTA1 = 1 and PCV1 = 1 and ROTA1 = 1 \\
10 weeks & NON-DEFAULT = 1 at 6 weeks and OPV2 = 1 and PENTA2 = 1 and PCV2 = 1 and ROTA2 = 1 \\
14 weeks & NON-DEFAULT = 1 at 10 weeks and OPV3 = 1 and PENTA3 = 1 and IPV = 1 \\
6 months & NON-DEFAULT = 1 at 14 weeks and VITA = 1 \\
7 months & NON-DEFAULT = 1 at 6 months \\
9 months & NON-DEFAULT = 1 at 6/7 months and MEAS1 = 1 and YF = 1 \\
12 months & NON-DEFAULT = 1 at 9 months and MANGA = 1 \\
18 months & NON-DEFAULT = 1 at 12 months and MEAS2 = 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Decision framework for defaulter and non-defaulter class definition. From Table 2, a child was considered a non-defaulter at birth if both BCG and OPV0 vaccines had been administered. At subsequent ages (6 weeks, 10 weeks, 14 weeks, etc. ), the child remained in the non-defaulter class only if the child was previously classified as a non-defaulter at preceding schedules, and the required vaccines for age had been received. For example, a week 6 non-defaulter must be a non-defaulter at birth and have received OPV1, PENTA1, PCV1, and ROTA1. This cumulative dependency ensures that a single missed vaccine at any point in the schedule results in the child being labelled as a defaulter from that point forward. We note that the HEPB vaccine birth dose was excluded from the decision rule, as its administration is conditional on maternal hepatitis B status [38]. Similarly, all the RTSS1 vaccine doses were excluded due to non-uniform rollout across the population during the MVPE study. Exploratory data analysis was conducted using univariate analysis with the chi-square test to assess the statistical significance of potential predictors of vaccination defaulter risk. The predictors examined included key socio-demographic characteristics as well as newly engineered features from vaccination timing windows and birth seasonality. Variable name mappings of the predictors analysed are presented in Table 12 in Appendix A. During the modelling phase, all variables found to be statistically significant in the univariate analysis were retained for inclusion in the predictive modelling phase. Additionally, variables identified as important in prior research were also incorporated, regardless of their statistical significance in the univariate analysis, to preserve theoretical relevance. However, a few redundant features and features that showed high collinearity were excluded. For example, the one-hot encoding technique split the child sex and rural-urban residency feature into two distinct variables, making one variable redundant. We also excluded cases of early vaccination deviations from the feature set for modelling due to their limited representation. EVD accounted for approximately 5% of the dataset used. Preliminary analyses showed that its sparsity resulted in unstable feature importance estimates across models, and inclusion reduced model robustness due to class imbalance. Train-test splitting was performed with a ratio of 70% training and 30% testing data. The models were trained on both the original imbalanced dataset and a synthetic version of the dataset obtained through data augmentation (DA) and the synthetic minority oversampling technique (SMOTE). These data enhancement techniques were used to mitigate limitations from a limited sample size and class imbalance.