## 2 Related Work
Article [14] conducted a survey on perinatal health predictors using AI, outlining and emphasizing the potential of AI-based algorithms and XAI techniques for the creation of novel prediction models, improved diagnosis, early detection, and monitoring of women during pregnancy, labor, and postpartum to advance research, clinical practice, and policies, and guarantee optimal perinatal health. According to article [15], model-specific or model-agnostic-based techniques are two broad categories under which XAI can be categorized. In general, many machine learning models, such as decision trees, rule-based models, and linear models, also referred to as transparent models or white-box models, are inherently interpretable. However, these straightforward models perform relatively worse than more complicated models. Model-specific and post hoc XAI algorithms could be created for each of the more complicated models, such as support vector machines (SVM), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and ensemble models. For instance, frequently employed techniques include feature relevance explanation, architecture change, explanation by simplification, and explanation by visuals. In general, these more sophisticated models might perform better while losing explainability. It is important to emphasize that, given the nature of the issues, simpler models may still outperform deep learning-based models while maintaining explainability. Table 1 summarizes the recent literature that addressed the maternal mortality problem. The table shows the datasets used for each study, the main methodology deployed, the best accuracy achieved, the region (city or state or country) in which the dataset was collected, and finally, the problem tackled by the research, addressing a monitoring technique to either predict maternal mortality risk or to predict region-level mortality. The table shows that most research achieves high results by using ensemble techniques, yet when the explainable AI approach was deployed, the results were much lower. All the previous literature focused on addressing the MMR at the patient level by monitoring expectant mothers or at a country level by studying the risk factors. In this work, the MMR problem is addressed from a global perspective. The first scope is a country-based evaluation, in which an assessment of each city/state is made, defining whether or not it is classified as high or low risk. Since the city information is affected by the individual monitoring of the patients, the second scope is to continuously monitor pregnant women's vitals and issue an alert for medium- and high-risk cases. This information was efficiently and securely transmitted to the healthcare center and recorded in the patient's medical record, thus allowing intervention whenever needed. Since the topics addressed are very sensitive at the country level and the patient level, it became important to choose an explainable model that would provide an analytical evaluation of the results. Thus, in this work, models providing high performance like deep learning are compared to explainable models to help decide whether the performance degradation is acceptable as opposed to the model's interpretability.