## 1 Introduction  
Undoubtedly, one of the problems facing the world is the morbidity and mortality of mothers because of pregnancy-related medical problems, which is still one of the major global challenges today. Every day, roughly 830 maternal fatalities worldwide result from problems during pregnancy or childbirth, and most of these phenomena could typically be avoided. By 2030, the Sustainable Development Goal 3 (SDG 3) of the United Nations seeks to increase maternal health and decrease the child and maternal mortality ratio (MMR) [1]. The MMR is a standardized method for calculating the number of women who die before, during, or within 42 days after giving birth to a child or while they are pregnant. The majority of these fatalities are caused by preventable factors, such as antepartum hemorrhage, postpartum hemorrhage, ruptured uterus, high blood pressure, severe bleeding, infection following pregnancy termination, and pulmonary embolism [2]. However, particularly in developing nations, the MMR has exceeded the predictions. Approximately 99% of maternal deaths from pregnancy and childbirth-related avoidable causes take place in underdeveloped nations [1; 3; 4].  
Artificial intelligence (AI) has been expanded to several healthcare applications, including patient records and diagnosis, management of health services, and prognostic medicine. Despite performing at a level comparable to humans, AI models still have a limited range of applications since their decisions are not always explainable. The limited adoption of AI in healthcare, particularly due to trust issues, can be addressed through explainable artificial intelligence (XAI). By providing clear explanations for model predictions, XAI can increase user confidence and promote the wider use of AI systems in healthcare [5].  
In 2022, a survey was conducted by [6] to examine current research and development viewpoints that make use of AI techniques to forecast the ideal delivery mode and identify different difficulties during childbirth. Their review study analyzed the chosen AI algorithms and their performances, highlighted the goals of the most recent studies on pregnancy outcomes using AI, and gave a synthesized perspective of the features employed, types of features, data sources, and characteristics. The survey also outlined opportunities for future research to support current efforts to lower the MMR and complacency rates using unsupervised and deep learning algorithms for prediction, identifying the unidentified causes of maternal complications, creating practical and useful AI-based clinical decision support systems for use by expectant mothers and healthcare professionals, improving datasets and their accessibility, and investigating.  
Accordingly, the contributions of this article can be summarized as follows:  
* A proposed XAI region-based evaluation framework, in which an assessment of each region (city or state) to be determined defines whether or not it is classified as high or low risk.
* An XAI individual continuous monitoring scheme of pregnant women's vitals which issues an alert for medium- and high-risk cases. The focus on using an explainable model is an advantage in the proposed framework compared to previous research.
* A proposed deep learning model that is to be trained for the regions dataset, and then, analyzed. The same model structure but with an updated input and output size is trained for the patient dataset and analyzed.
* A proposed efficient and secure transmission of the data collected from pregnant women to the healthcare center.
* A performance assessment of the proposed deep learning model, including an XAI analysis of the results.  
The rest of the article is organized as follows: first, the related work is presented. Afterwards, the proposed methodology for region-level and patient-level monitoring and efficient information transmission schemes is illustrated. The results are then reported and discussed. Finally, the conclusions and future work are presented.