## 2 Related Work
Additionally, the authors state that machine learning has improved early risk assessment for gestational diabetes and preterm birth, yet obstacles with balancing data and interpretation difficulties alongside generalization issues block its extensive adoption [29]. Machine learning explainability techniques deliver information about the prediction processes used by models. In their research, [PERSON], [PERSON] et al. compare SHAP and Grad-CAM for human activity recognition through a comparative analysis. These researchers demonstrate that SHAP provides optimal feature-level explanations through its quantitative analysis, which lets users track individual input feature contributions; thus, it excels at structured and tabulated datasets. On the other hand, the visual nature of Grad-CAM produces clear heatmaps showing which image sections drive the model toward its decision outcome [30]. [PERSON] et al. introduce an extensive survey on the application of machine learning (ML) and deep learning (DL) box models in maternal healthcare, with an emphasis on reducing maternal mortality. The survey classifies models into white, gray, and black boxes based on interpretability and estimates their efficacy across numerous predictive tasks, such as fetal monitoring, pre-eclampsia detection, and childbirth outcome prediction. Composed of 71 studies, the review illuminates the growing role of AI-driven clinical decision support systems (CDSSs) while also observing their underutilization in the existing research. The researchers underscore the importance of manipulating diverse data sources, including electronic health records, imaging, and behavioral data, and advise to improve model transparency and ethical deployment, explicitly in resource-constrained settings. Medical imaging stands better due to the system that identifies the main differences in images above what traditional assessment tools can detect [31; 32]. [PERSON], [PERSON] et al. present a deep learning model that uses EHR data to forecast preterm birth delivery with excellent accuracy levels while creating time for necessary preventive healthcare actions for at-risk pregnancies [33]. The research by [PERSON], [PERSON] et al. discusses that the implementation of CDSSs adds multiple capabilities at different levels of health delivery, which minimizes errors in medication prescription and adverse incidents while enhancing clinical oversight, lowering costs, delivering administrative tasks, supporting diagnosis functions and decision systems, and simplifying medical operations [34]. The research by [PERSON], [PERSON] et al. examines a DL-based CDSS for gestational diabetes management that resulted in better patient results and decreased healthcare expenses. The research demonstrates that DL has the potential to advance pregnancy medical care through its ability to deliver prompt data-based insights. The studies showcase obstacles as well as benefits since healthcare systems require better interface design and adaptation of DL models into the current healthcare infrastructure [35]. These comparative approaches are summarized in Table 1, highlighting their respective outcomes, benefits, and limitations in prenatal care. [PERSON] et al. form a web-based maternal health risk forecasting model using machine learning classifiers and IoT-collected vital signs. Within the spectrum of models tested, an ensemble of random forest, AdaBoost, and Gradient Boosting with soft voting secures the highest accuracy of 81.5%. The research highlights the usefulness of ensemble learning in improving pregnancy risk classification for clinical decision support [36]. [PERSON] et al. propose PregnancyLine, a visual analysis system that improves pregnancy care and risk communication by revealing temporal medical data through intuitive visual metaphors. The system assists doctor-patient interaction, promotes early detection of