## 3 Architectural System Design
### System Architecture
#### 3.8.1 ResRNN-Net and MultiScaleFusion-Net
**Feature 39 (Prenatal_Care)** drove the prediction towards the negative class, while **Feature 22 (Preg_iron)** influenced it positively. The plot emphasizes the model's reliance on clinically significant features, justifying its conformity to domain expertise. ### Grad-CAM Visualization

To effectively demonstrate the Grad-CAM approach, an alternative image dataset was employed: the Fetal Planes Ultrasound Dataset [41]. This dataset includes ultrasound images of fetal structures, categorized into different planes, such as fetal thorax, fetal brain,

Figure 23: SHAP waterfall for ResRNN-Net. Figure 24: SHAP waterfall for MultiScale Fusion-Net. fetal abdomen, and maternal cervix. A ResNet-18 model was fine-tuned on this dataset, achieving high accuracy in classifying the fetal planes. The Grad-CAM technique was then applied to visualize the regions that influenced the model's predictions for each image. As shown in all the images in Figure 25, the heatmaps effectively highlight the critical regions within the ultrasound scans, such as the thoracic cavity in the fetal thorax images or the brain structures in the fetal brain images. This visualization confirmed that the model was making predictions based on medically relevant areas rather than irrelevant background features. These results demonstrate the potential of Grad-CAM for enhancing interpretability in medical imaging tasks. While it was not directly applicable to the tabular pregnancy dataset, the image-based evaluation provided a clear understanding of how Grad-CAM highlights important features in deep learning models. ## 5 Conclusions

Worldwide healthcare initiatives consider maternal health to be their foundational building block while affecting public health in addition to social and economic development. The implementation of technology, especially artificial intelligence (AI), in maternal healthcare presents a complete solution for the existing challenges. In this study, we have constructed a stable deep learning framework to predict pregnancy complications based on the NFHS-5 dataset. By adopting a systematic pipeline with data preprocessing, feature selection, model building, and evaluation, the project achieved identifying high-risk pregnancies with great accuracy and explainability. ResRNN-Net and MultiScaleFusion-Net proved to be the top-performing architectures, utilizing Bi-LSTM, CNNs, and attention techniques to identify subtle relationships in the dataset. ResRNN-Net obtained the best F1-score (82%), balancing precision and recall,

Figure 25: Confusion matrix and ROC–AUC curve for ResRNN-Net. while MultiScaleFusion-Net provided similar performance (81%) with quicker inference. Conventional machine learning algorithms such as XGBoost and ensemble classifier lagged behind the deep learning strategies, highlighting the significance of sophisticated architectures for high-complexity clinical predictions. The multimodal model further enhanced predictive power by integrating tabular, image, and time-series data, ensuring higher recall (100%), making it ideal for risk-sensitive applications. The incorporation of explainable AI (XAI) methods, including SHAP and Grad-CAM, additionally improved the model explainability, ensuring that the essential features influencing the predictions were interpretable and clinically meaningful. The deep learning models not only achieved better prediction accuracy but also provided interpretable results, so the system is quite suitable for deployment in maternal health monitoring in the real world. Fostering the implementation of predictive models in maternal healthcare requires negotiating numerous important directions. Broadening datasets to incorporate more divergent populations--across geographic regions, age groups, and volatile risk factors--will complement the adaptability and tenacity of these models.