## 2 Related Work
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Techniques** & **Outcomes** & **Benefits** & **Limitations** & **Reference** \\ \hline XGBoost + Neural Networks & High precision in predicting stillbirth risks & Effectively handles large datasets, improves clinical decisions & Model interpretability issues, sensitive to data quality & [2] \\ \hline Wireless Sensors + Machine Learning & Real-time tracking of maternal health parameters & Enables remote monitoring, beneficial for low-resource settings & Data security concerns, variability in sensor accuracy & [9] \\ \hline CNN + LASSO Grad-CAM & 92\% accuracy in fetal anomaly detection & Early identification of fetal defects, improved diagnosis accuracy & Requires large labeled datasets, high computational demand & [10; 28] \\ \hline Deep Learning Transfer Learning & 81.34\% accuracy, 88\% precision, 80\% recall & Enhances prediction of hypertensive disorders during pregnancy & Requires extensive pre-trained models, risk of bias in transfer learning & [25] \\ \hline Deep Neural Networks + SHAP & Improved explainability of preterm birth risk factors & Provides interpretable results, identifies key risk indicators & Computationally intensive, explainability still limited & [27] \\ \hline CNN + LSTM + SHAP & High accuracy in predicting diabetes risk during pregnancy & Captures time-series variations, improves patient-specific monitoring & Requires high-quality time-series data, model complexity & [29] \\ \hline Random Forest + SVM & High-risk pregnancy classification model & Enhances early identification of maternal mortality risks & Less effective with complex, high-dimensional data & [31] \\ \hline Logistic Regression + Decision Trees & 88.5\% accuracy in preterm birth prediction & Simple model with interpretable results & Lower accuracy compared to deep learning models & [33] \\ \hline Decision Trees + Driferent algorithms & Highlighted gaps in explainability & Balances simplicity with predictive power & Struggles with unstructured clinical data & [34] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different techniques in prenatal care. abnormalities, and aids pregnant women in understanding their examination results and associated risks. The evaluation results display improved comprehension and reduced anxiety among users [37]. Continuous accumulation of data provides a broad array of information on maternal health and makes it easier for healthcare examiners to examine the changes in pregnancy health over a given period. The integration of DL and IoT systems improves the predictive analysis of societies while availing the early prenatal facilities that help organizations to achieve better health of new mothers and their babies [38]. XAI develops methods to make artificial intelligence frameworks explain their choices in essential environments, including healthcare. However, various limitations block the widespread acceptance of XAI systems. The primary difficulty faced by interpretable artificial intelligence involves choosing between understandable prediction systems that provide limited precision and advanced prediction methods that offer enhanced precision [39].