## 4 Discussion
Beyond implementation in LMICs, the proposed system's seamless integration with existing community health networks--leveraging patient-specific electronic health records (EHR) makes it a valuable tool for improving healthcare access in rural and underserved populations across the United States [37]. Although our study contributes significantly to maternal health research by developing machine learning models that use sparse data and features to predict maternal health risks and provide insights that can enhance the adoption of IoT devices and m-health applications in LMICs and resource-constrained settings, this study has limitations. Firstly, the dataset used in this study might not fully represent the diversity of maternal health cases observed across various LMICs, potentially limiting generalizability. Specifically, this study uses a relatively small dataset (~1000 data points) from one South Asian country and misses out on sub-Saharan Africa, which has the most LMICs. Furthermore, from a modeling standpoint, while the ensemble model improved the classifying accuracy compared to the individual models, we observed that the ensemble model did not perform well in classifying mid-risk cases where it reported lower recall. We hypothesize two potential reasons for this: the limited dataset, which includes roughly 1000 patients and, more critically, the sparse features available in the data. The latter is the most significant factor because it could be possible that the features used in this study are not clear enough to delineate the difference between the mid-risk from low- and high-risk, causing an overlap leading to incorrect predictions. From a clinical aspect, this could impact large-scale implementation. Finally, beyond the challenges discussed above, although relying on limited data to classify maternal health risks makes it easy for scalability in resource-constrained settings, this approach could overlook other critical data points, such as socio-economic, behavioral and medical history data, that can potentially improve classification accuracy. Future research should prioritize testing and validating the ensemble model developed in this research using larger datasets with more features from diverse LMICs, mainly from sub-Saharan Africa--where maternal health disparities are most pronounced and often overlooked. Furthermore, investigating additional features not included in this data could shed more light on predictors of maternal health risk, especially with the mid-risk cases. Some features worth exploring include social determinants of health, prior medical history, and comorbidities. Beyond investigating larger datasets, it is critical to explore diverse data from various LMICs and other resource-constrained areas from developed countries where features such as accessibility to medical care, community of residence etc., can potentially act as a strong predictor. Furthermore, piloting the system in different healthcare settings, including larger hospital systems, private practices etc., is crucial to assessing the algorithm's potential in reducing maternal morbidity and mortality. Research should also evaluate its integration with existing community health frameworks and explore its scalability to other health conditions. While these are some potential factors that future researchers should consider, implementation may face challenges, including limited internet connectivity, digital literacy barriers, adoption by healthcare providers and communities, and data security concerns. Understanding local capabilities, such as digital literacy levels and the willingness of traditional birth attendants (TBAs) and patients to adopt intelligent technology, is essential for successful integration into prenatal care. Finally, researchers should develop deployment frameworks using secure mobile or cloud-based platforms to improve accessibility and scalability in LMICs.