## 3 Architectural System Design
### System Architecture
#### 3.8.1 ResRNN-Net and MultiScaleFusion-Net
### Multimodal Model Performance

The multimodal model was evaluated in two configurations: tabular-only and integrated (tabular + image + EHG). The tabular-only model achieved 80% accuracy with balanced precision and recall, making it a strong baseline. However, it lacked deeper insights from imaging and physiological signals. The integrated model improved recall (90%), ensuring fewer missed complications, but at the cost of lower precision (55%), leading to more false positives. The integrated model leveraged diverse data for richer predictions. Its increased training complexity and computational demands require further optimization for real-world deployment. Figure 16: Confusion matrix and ROC–AUC curve for ResRNN-Net. Figure 17: Confusion matrix and ROC–AUC curve for MultiScale Fusion-Net. ### Discussion and Comparative Analysis

Although competitive performance was shown by all the models, ResRNN-Net and MultiScaleFusion-Net were remarkable due to their higher recall and F1-scores. TabNet was a very interpretable choice, and the MLP was computationally efficient, with excellent accuracy. The model selection was based on the deployment context--ResRNN-Net for clinical sensitivity, TabNet for interpretability, and an MLP for real-time resource-scarce scenarios. The integrated multimodal model, despite its complexity, reinforced the value of combining tabular, image, and time-series data for robust pregnancy complication prediction. Table 10 provides a critical evaluation of all implemented models, comparing key metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Statistical and Deployment Oriented InsightsTo support the experimental findings with practical insights, we examined computational efficiency and deployment feasibility. Both ResRNN-Net and MultiScaleFusion-Net were trained with the best hyperparameters obtained from a random search (learning rate = 0.0005; batch size = 64). * **Training Time:*
* ResRNN-Net took approximately 517.21 s to train for three epochs with early stopping. * MultiScaleFusion-Net completed training in 113.8 s.
* The quicker convergence of MultiScaleFusion-Net highlights the efficiency of its architecture with multiscale convolutional layers. * **Inference Time:*
* For inference on 1000 samples:
* ResRNN-Net took 2.62 s.
* MultiScaleFusion-Net took 1.41 s.
* This demonstrates the effectiveness of MultiScaleFusion-Net for real-time applications. * **Trade-offs Between Models*
* ResRNN-Net provided higher precision and F1-scores but required longer training and inference times due to recurrent layers. * MultiScaleFusion-Net achieved similar performance with faster training and inference thanks to efficient convolutional layers, making it well suited for real-time applications. * **The TabNet and MLP*
* models reached 78-80% accuracy but lack the advanced feature extraction of deep learning architectures. Their simplicity aids interpretability but limits them in handling complex data. * **The multimodal model*
* (tabular, image, and time-series data) had lower accuracy (60%), whereas the tabular-only model performed well, achieving (80%) accuracy with stable precision and recall. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **ROC-AUC** \\ \hline ResRNN-Net & 0.80 & 0.73 & 0.93 & 0.82 & 0.87 \\ MultiScaleFusion-Net & 0.79 & 0.72 & 0.94 & 0.81 & 0.86 \\ TabNet & 0.78 & 0.89 & 0.78 & 0.78 & 0.85 \\ MLP (PyTorch) & 0.79 & - & - & - & - \\ Multimodal Model & 0.80 & 0.75 & 0.90 & 0.81 & 0.80 \\ Multimodal Model (Integrated 3 datasets) & 0.60 & 0.55 & 1.00 & 0.71 & 0.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Critical evaluation of models implemented. Deployment Recommendation: MultiScaleFusion-Net is preferred for clinical deployment due to its speed and scalability. ResRNN-Net remains useful where precision outweighs efficiency. The model selection should align with the computational capacity, latency constraints, and prediction priorities.