## 4 Discussion  
This study investigated the potential of developing machine learning models to predict maternal health risks using sparse data and features, such as vital signs in resource-constrained settings such as LMICs. Using minimal data and sparse features collected from pregnant women in the rural areas of Bangladesh using an IoT-based risk monitoring system, we demonstrate the potential of sparse data trained with machine learning algorithms to predict maternal health risks. Among the individual models, XGBoost and CatBoost consistently achieved the highest performance, with XGBoost showing the highest accuracy (86.7%) and F1 scores (0.93) for high-risk cases under stratified sampling. Across sampling techniques, we observed that stratified sampling consistently provided better performance compared to random sampling. Furthermore, we observed that the ensemble model developed using random forest, XGBoost, and CatBoost further improved overall performance, achieving an accuracy of 87.2% and significant improvements in precision, recall, and F1 scores across all risk categories, supporting our hypothesis. This observation highlights the need to combine various machine learning models to leverage their specific strengths and sampling methods to improve accuracy and robustness. Our findings align with prior studies that have developed and deployed machine learning algorithms for risk prediction in healthcare, such as cardiovascular disease and diabetes. However, using sparse data, our work addresses a significant gap by applying these techniques to maternal health risk prediction in LMICs, which is often overlooked. Figure 4: Model accuracy using stratified 10-fold cross-validation. This is a critical gap as most prior studies rely extensively on large datasets, which often include subjective and objective data, including medical history, to achieve high prediction accuracy. Maternal health data often lacks comprehensive longitudinal records and does not contain highly variable but relevant socio-economic and cultural factors. Findings from this research contribute to the growing maternal health research by emphasizing the capability of minimal data, such as vital signs, and integrating it with machine learning algorithms to classify maternal health risks with high accuracy. Furthermore, observations from this study highlight that vital sign disturbances can be indicative of imminent decline from a plethora of conditions in pregnant mothers, including cardiomyopathy, thrombotic embolism, hypovolemic shock secondary to hemorrhage, underlying cardiac and coronary conditions, infections, and hypertensive disorders of pregnancy [32; 33; 34; 35]. These conditions remain the leading cause of preventable maternal deaths and hospitalizations globally [36]. Developing an ensemble model that combines the best-performing individual models that leverages a stratified sampling approach and uses sparse data is novel in maternal health research. As seen from the results, this was the best-performing model because using stratified sampling ensured consistent class representation, reduced biases, and improved model performance compared to random sampling. Along with the sampling method, the ensemble model that combined random forest, XGBoost, and CatBoost addressed the individual limitations of the model, leading to an improvement in the overall predictive performance across all risk levels. Specifically, we observed that this model was more accurate in classifying high-risk cases while maintaining robust performance for low- and mid-risk categories. This ability of the ensemble model to accurately clarify high-risk cases allows for quick medical intervention, which is crucial in resource-constrained settings, where access to healthcare and transportation are often limited. Moreover, its ability to accurately classify maternal risk using sparse data makes the developed model deployable, cost-effective, and practical for implementation in LMICs.