## 4 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **[PERSON] et al. [25]** & **Our Study** \\ \hline Original data size & 643 & 13,724 \\ \hline Data enhancement & None & \begin{tabular}{c} SMOTE \\ Bootstrap resampling \\ \end{tabular} \\ \hline Number of features & 16 & 158 \\ \hline Feature engineering depth & One-hot encoding & \begin{tabular}{c} One-hot encoding \\ Aggregation \\ Temporal feature \\ transformations \\ \end{tabular} \\ \hline \multirow{4}{*}{Modelling techniques} & Logistic regression & \
\begin{tabular}{c} Logistic regression \\ Support vector machine \\ Random forest \\ \end{tabular} \\  &
\begin{tabular}{c} Random forest \\ Gradient boosting machine \\ Extreme gradient boosting \\ Artificial neural networks \\ \end{tabular} \\ \hline \multirow{4}{*}{Evaluation methods} & Precision & Precision \\  & Recall & Recall \\ \cline{1-1}  & F1 score & F1 score \\ \cline{1-1}  & accuracy & accuracy \\ \cline{1-1}  & ROC AUC & ROC AUC \\ \hline Performance range & 56–75\% & 77–95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 15: A comparison with previous works on model performance. As seen in Table 15, our study used a much larger dataset (13,724 records vs. 643), applied multiple data enhancement techniques (SMOTE and bootstrap resampling), and incorporated diverse feature engineering strategies, including temporal encoding of vaccine delays and seasonality. In contrast, [PERSON] and colleagues relied on a smaller set of socio-demographic features and evaluated fewer algorithms. Our use of more diverse and sophisticated machine learning models also contributed to the higher rigour of the analysis, with AUC values as high as 95% compared to a maximum of 75% in the previous study. These results highlight the importance of data scale, extensive feature design, computational depth, and model diversity in improving the prediction of vaccination defaulters. We acknowledge several limitations that may influence the outcomes of this study. First, this study relies on documented vaccination records as a proxy for vaccine receipt. This approach assumes that individuals without documented evidence of vaccine administration were classified as defaulters, which may not account for instances where vaccinations were administered but not recorded. As such, there is the possibility of vaccination coverage underestimation due to false classification of such children as defaulters. Also, the vaccination time window computations were performed with reference to the child's date of birth rather than the date of consecutive vaccine schedules. As such, an earlier default which might have required real-world schedule adjustments may have staggered effects on how the timeliness of subsequent schedules was classified. In addition to this, there were several instances where the recorded vaccination dates varied across antigens within a specific window due to transcription errors. So, it would be inaccurate to definitively attribute all date deviations to genuine delays in administration. Secondly, the surveys analysed were conducted within the same geographic region over a 24-month period and so the possibility of data duplication cannot be entirely ruled out. With respect to the introduction of timely vaccination windows, we agree with [19] that continuous data representations would have been more informative compared to the binary vector indicators of delayed vaccination used. However, inaccuracies in the vaccination cards and paper-based hospital registries from which the survey data were extracted constrained the effectiveness of performing accurate continuous datapoint calculations on the length of vaccination timeliness. Also, while birth season emerged as a significant predictor of defaulting, this finding reflects an association rather than a confirmed causal relationship. Birth during the dry season may coincide with increased household mobility, reduced health service accessibility, or competing economic priorities, all of which may influence vaccination adherence.