## 1 Introduction
Simple metrics like blood pressure and heart rate, which are universally collected during prenatal visits, require minimal medical training to measure and could provide meaningful insights into maternal health risks [13; 14]. For example, elevated blood pressure is a well-established marker for preeclampsia, a leading cause of maternal mortality, while abnormal heart rates can signal underlying cardiovascular issues [15]. Prior studies have demonstrated the efficacy of vital signs as predictors for health risks in other domains, such as cardiovascular health, where they have been used to predict hypertension and heart disease with high reliability [16; 17]. Additionally, in acute care and hospital settings, vital signs can serve as early indicators of imminent patient deterioration [18; 19]. Drawing from these parallels, leveraging vital signs as potential predictors in maternal health contexts offers an opportunity to develop predictive models that are both effective and accessible. These solutions could significantly impact LMICs, where advanced diagnostic tools are often unavailable, and healthcare providers must rely on easily measurable and interpretable data to guide interventions. By focusing on the scalability and simplicity of vital sign-based risk prediction tools, health systems can empower providers to detect and address maternal health complications early and remotely, ultimately reducing maternal mortality in resource-limited or constrained settings [20; 21]. The last several years have seen a significant increase in translational research where researchers and practitioners have developed and deployed machine learning algorithms to predict outcomes and manage diseases such as diabetes, cardiovascular conditions, and sepsis, where it has demonstrated superior accuracy in identifying high-risk patients compared to traditional methods [22; 23; 24]. Unlike traditional statistical models, machine learning algorithms' ability to handle complex, nonlinear relationships and high-dimensional data allow them to uncover patterns and interactions that might otherwise go unnoticed [25]. These advantages make ML particularly promising for risk assessment and decision-making in clinical settings. While ML is increasingly utilized in healthcare, its application to maternal health remains limited, particularly in low- and middle-income countries (LMICs) [26; 27]. However, there is significant potential for ML models to address maternal health challenges by enabling early detection of risks such as preeclampsia and postpartum hemorrhage, even in resource-constrained environments. Integrating ML into low-resource settings is becoming feasible through mobile or cloud-based applications, which can bring advanced analytics to frontline healthcare providers and improve patient outcomes and health equity [28; 29; 30]. As discussed above, despite the significant maternal mortality burden in LMICs, there is a notable lack of research focused on leveraging minimal data, such as vital signs for maternal health risk prediction, focusing on these areas. Additionally, current research studies often overlook how machine learning algorithms could help address these gaps, especially in resource-constrained settings with limited access to advanced diagnostics. One primary reason could be the lack of m-health solutions deployed in these areas, which limit the data availability for researchers to develop models. Additionally, most machine learning models require large datasets and might be unable to train and learn using sparse data and features. Furthermore, even across developed countries, the viability and comparative performance of different ML algorithms in predicting maternal health risks remain unexplored. To address these research gaps identified through the literature review, we formulate two objectives for this study.