## 2 Related Work  
The research by [PERSON] et al. presents two novel ensemble models, Echo Dense Inception Blending (EDI-Blend) and Dense Reservoir Inception Modular Network (DRIM-Net), aimed at predicting miscarriage risks by applying advanced data analytics techniques, particularly deep learning (DL). The study utilizes a balanced dataset processed through the hybrid balancing technique NearSMOTE and features selected using the least absolute shrinkage and selection operator (LASSO). The models are validated using 10-fold cross-validation, with EDI-Blend achieving notable performance metrics such as 0.732 accuracy and 0.721 ROC-AUC, while DRIM-Net outperformed with 0.769 accuracy and 0.837 ROC-AUC. The results highlight the potential for these models to aid healthcare professionals in early detection and intervention, ultimately improving patient outcomes [1]. Through real-time data collection, healthcare providers obtain pregnancy-related health information about their patients that helps them to spot potential medical complications early. Wearable sensors possess the capability to send notifications about vital sign changes that indicate pre-eclampsia or gestational diabetes to both patients and healthcare staff [11]. [PERSON] et al. discuss a novel hypertension prediction model by merging deep learning (DL) with transfer learning (TL) techniques to escalate early detection and lessen the risk of cardiovascular diseases. Their perspective entails fine-tuning a pre-trained Feed-Forward Deep Neural Network (FFDNN), initially prospered for diabetes prediction using the PIMA dataset, and modifying it for hypertension prediction using the PPG-BP dataset. They link a hybrid feature selection strategy integrating statistical methods and domain expertise, choosing four major features--systolic and diastolic blood pressure, heart rate, and age--to upgrade the model performance. The TL strategy incorporates freezing pre-trained layers and re-training recently added layers to confirm efficient knowledge transfer while decreasing training time. The final model arrived at an accuracy of 81.34%, with 88% precision and 80% recall, evidencing a strong balance between identifying hypertensive patients and reducing false positives. This effort focuses on the potential of transfer learning to reshape the existing healthcare models for associated diagnostic tasks, especially in data-scarce environments [25]. [PERSON] et al. address the requirement to provide understandable explanations, which faces additional challenges because various stakeholders, including patients, clinicians, and developers, possess different needs. Implementing XAI into clinical operational systems becomes difficult due to the requirement of avoiding workflow disruptions [26]. [PERSON], [PERSON] et al. discuss various deep learning approaches that have multiple applications in pregnancy monitoring to solve crucial maternal and fetal health problems. The prediction of preterm birth stands as the most essential deep learning application because it prevents neonatal mortality and morbidity. Studies conducted by [27] confirm DL models' achievement to accurately predict risk of extreme preterm birth. The research by [PERSON], [PERSON] et al. extensively assesses deep learning applications for fetal anomaly detection, where CNNs demonstrate high precision for anomaly recognition. The detection models achieve outstanding results in identifying minor structural anomalies, which leads to early identification of diseases and quick medical help [28]. The research by [PERSON], [PERSON] et al. presents an extensive review about machine learning systems for pregnancy outcome prediction while demonstrating their ability to improve maternal healthcare with data-based knowledge. The work integrates previous models into hierarchies that group together models based on clinical parameters and demographic factors, fetal health indicators, and more features.