## 6 Conclusions  
In this article, the MMR problem was addressed by building a model that has a global perspective and monitors it at the region and patient levels. The region-based assessment evaluated each region, and accordingly, the risk level was calculated. The second area of focus was providing continuous vital signs monitoring for expectant women in high-risk areas to issue alerts for medium- and high-risk instances. The patient's medical records at the data center and the hospital contain a record of this alert, which is efficiently and securely delivered through an efficient communication system, and then, recorded to allow for future intervention. It became crucial to select an explainable model that would offer an analytical evaluation of the outcomes because the themes treated are extremely sensitive both at the national and patient levels. To assess the trade-off between model performance and interpretability, this study analyzed high-performance deep learning with explainable models. The results demonstrated that the proposed CNN-GRU model achieved superior performance to state-of-the-art benchmarks and other published research while offering explainable outcomes. The performance of both systems was evaluated by analyzing the importance of key features, identifying feature relationships, and examining the underlying methodology used to determine risk. This analysis allowed for a comprehensive understanding of how each model arrived at its predictions. The explainability of the chosen model will enhance its usage among medical staff. As for future work, more data will be collected to include further details of each patient and to which region they belong. Accordingly, a deeper level of interpretation could be included to link the state-level and patient-level risks. This will help in gaining a better understanding of the risk variations within different regions.  
Conceptualization: S.N.S.; data curation: S.N.S. and R.A.O.; formal analysis: S.N.S., R.A.O. and Y.N.M.S.; investigation: S.N.S. and R.A.O.; methodology: S.N.S. and R.A.O.; resources: S.N.S., R.A.O., Y.N.M.S. and M.N.E.; software: S.N.S. and R.A.O.; validation: S.N.S. and R.A.O.; visualization: S.N.S. and R.A.O.; resources: S.N.S., R.A.O., Y.N.M.S. and M.N.E.; writing--review and editing, S.N.S., R.A.O., Y.N.M.S., and M.N.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that supports the findings of this study are available from [[https://www.kaggle.com/datasets/csafrit2/maternal-health-risk-data](https://www.kaggle.com/datasets/csafrit2/maternal-health-risk-data)]([https://www.kaggle.com/datasets/csafrit2/maternal-health-risk-data](https://www.kaggle.com/datasets/csafrit2/maternal-health-risk-data)) accessed on 9 October 2024 and from [[https://www.kaggle.com/datasets/sourabhshatri/maternal-health-dataset](https://www.kaggle.com/datasets/sourabhshatri/maternal-health-dataset)]([https://www.kaggle.com/datasets/sourabhshatri/maternal-health-dataset](https://www.kaggle.com/datasets/sourabhshatri/maternal-health-dataset)) accessed on 9 October 2024.  
The authors declare no conflicts of interest.