## 1 Introduction  
Ongoing research has accentuated the significant rates of infant mortality due to vaccine-preventable diseases in low- and middle-income countries (LMICs). In sub-Saharan Africa, estimates indicate that these conditions result in approximately one million infant deaths annually [1]. As a result, reducing infant mortality associated with vaccine preventable diseases is a public health priority for many sub-Saharan African countries, where the burden is highest [2]. An aspect of high research interest is the issue of vaccination defaulting, where some children fail to complete the full vaccination schedule [3]. Defaulting can leave children vulnerable to diseases and undermine efforts to inhibit the spread of vaccine-preventable diseases, which in turn undermines mortality reduction. Common predictors of childhood vaccination defaulters are well documented in the literature [4; 5; 6], which include maternal literacy, household wealth, geographic disparities, and healthcare access. It is also understood that the relevance and weight of these predictors vary across populations. For example, [PERSON] and colleagues [7] identified maternal education level, type of community settlement, and the mother's knowledge of childhood vaccination benefits as the most important determinants of vaccination defaulting in Ethiopia, while [8] identified place of residence, delivery location, and ANC visits as the key predictors of vaccination defaulter risk in Mali. The task of identifying the predictors of a health outcome invariably requires the use of efficient analytical methods to assess complex population structures, health behaviour patterns, social networks, temporal events, and locational factors. While traditional statistical approaches remain valuable, they are often characterised by rigid assumptions [9] which tend to impose limitations on the extent to which they can adapt to non-linear, high-dimensional data. The new paradigm of machine learning tends to offer adaptive techniques with less rigid assumptions in handling large-scale variables [10]. Machine learning techniques provide efficient ways of using computational methods to analyse larger datasets, also with minimal human intervention during the process [11]. A wide range of machine learning techniques exist, spanning decision trees [12; 13], ensemble classifiers [12; 14; 12], support vector machines [12], neural networks [8; 14], and deep learning models [15]. These techniques are already highly valued in the field of clinical diagnostics [16]. There is also significant evidence on the contributions these novel techniques are making to improve the planning and impact of public health [17; 18], especially in low-resource settings. Increased interest in the use of machine learning models for the analysis of childhood vaccination defaulter risk predictors within resource-constrained environments is evidenced in existing studies [13; 14]. Yet applications so far have adopted basic socio-demographic representations. By relying on such basic representations, a large amount of valuable information embedded within complex demographic, socio-economic, and environmental data is wasted, effecting our capability to improve the performance of learning models. One important aspect that has been underexamined in machine learning studies within the sub-Saharan African context is temporal events. For instance, the order of vaccination, as well as the time interval between them, often carries complex information that may be crucial for predicting defaulter risk [19]. At the same time, many LMICs, particularly within the sub-Saharan African region, experience multiple climatic patterns with ecological zonal variations [20; 21], which could have important implications for healthcareaccess and delivery in underserved areas [22]. These temporal characteristics, however, are highly sensitive to inaccuracies and can produce extreme or inconsistent results if timestamps are modified.