## 2 Related Work  
Several research articles have addressed the problem of the MMR. For instance, pregnant women in Afghanistan face several difficulties, including a shortage of medical centers, a lack of health information management systems in the majority of clinics and hospitals, a lack of doctors, cultural concerns, and issues with infrastructure and transportation, among others [7]. To address the issue of the MMR in Afghanistan, ref. [7] offered four proposals for using information communication technology (ICT), including the implementation of awareness campaigns through ICT, medical decision support systems, telemedicine, and healthcare information systems by applying different data mining (DM) and machine learning (ML) methods. The Internet of Things (IoT) can be used in sectors that are vital to society like the healthcare sector [8; 9]. Consequently, ref. [8] presented an efficient monitoring system for expectant mothers, primarily in remote areas of a poor country, with the aid of wearable sensing-enabled technology, notifying the expectant mother and her family about their health concerns. In the same context, for efficient tracking and forecasting of a pregnant woman's risk level in Bangladesh, a collection of data on maternal health was assembled from various sources (IoT devices, web portals, and hospitals in Bangladesh) and saved on a cloud server by [9]. Their modified decision tree (DT) algorithm achieved a 97% accuracy in predicting the risk level of pregnant women, which was the best compared to various ML classification techniques. In 2021, ref. [10] conducted another MMR study in Bangladesh using bagging ensemble classifiers, a novel approach in the field of birth mode prediction built on some traditional ML algorithms. In their research, bagging ensemble classifiers were used to analyze the performance of four ML techniques (DT-Bagging, KNN-Bagging, NB-Bagging, and SVM-Bagging). The outcomes demonstrated that, in this area, bagging ensemble models outperformed conventional models. In the same context of studying the MMR in different countries, a nested ensemble system based on stacking and voting was proposed by [11] for the prediction and study of the MMR in India. By applying several classification algorithms, the provided nested ensemble includes base learners and meta-learners. The prediction results were then assessed using K-fold cross-validation, facilitating the statistical distribution of results. The experimental results of the study demonstrated that the best model for categorizing the maternal health data into high MMR and low MMR used random forest as the base learner in combination with two other base learners, logistic and PART, and two meta-learners, Hoeffding and REPTree. Furthermore, ref. [12] used an electrocardiogram (ECG)-based DL model on a cohort of women examined at the Mayo Clinic who were pregnant or in the postpartum phase to diagnose cardiomyopathy, which is a significant risk to maternal health. The area under the receiver operating characteristic curve (AUC), along with accuracy, sensitivity, and specificity measures, was used to assess the performance of their model to compare it with natriuretic peptides and a multivariable model made up of clinical and demographic factors. While these studies have made significant contributions to the field of maternal health, there is a growing need for interpretable and explainable AI models to improve trust and transparency in healthcare decision making. Article [13] has employed several machine learning models to classify the region-level risk. These models include logistic regression, classification and regression tree classifier, artificial neural networks, support vector machines, and random first. Their testing results showed that random forest and logistic regression had the best results. They also included an analysis of the selected models to show the explainability and importance of the features.