## 3 Architectural System Design
### System Architecture
#### 3.8.1 ResRNN-Net and MultiScaleFusion-Net
Additionally, we plan to conduct ablation studies using combinations of two modalities to better understand each data type's contribution and refine our integration strategy further. Table 9 presents a consolidated overview of the key hyperparameters used across the ResRNN-Net, MultiScaleFusion-Net, and multimodal models. It details the architectural components, such as recurrent layers, convolutional blocks, and fusion strategies, along with their associated configurations, like unit sizes, activation functions, and optimizers. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Learning Rate** & **Batch** & **Optimizer Accuracy** & **F1-Score** & **Precision** & **Recall** & **ROC-AUC** \\ \hline
0.0010 & 16 & [PERSON] & 0.5263 & 0.6667 & 0.5294 & 0.9 & 0.5056 \\
0.0010 & 16 & [PERSON] & 0.5789 & 0.5556 & 0.6250 & 0.5 & 0.5833 \\
0.0010 & 32 & [PERSON] & 0.5263 & 0.6667 & 0.5294 & 0.9 & 0.5056 \\
0.0010 & 32 & [PERSON] & 0.5263 & 0.6897 & 0.5263 & 1.0 & 0.5000 \\
**0.0005** & **16** & **[PERSON]** & **0.7895** & **0.8333** & **0.7143** & **1.0** & **0.7778** \\
0.0005 & 16 & [PERSON] & 0.6316 & 0.6667 & 0.6364 & 0.7 & 0.6278 \\
0.0005 & 32 & [PERSON] & 0.6316 & 0.7407 & 0.5882 & 1.0 & 0.6111 \\
0.0005 & 32 & [PERSON] & 0.5789 & 0.5000 & 0.6667 & 0.4 & 0.5889 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Hyperparameter optimization results for the multimodal model [integrated]. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Performance Metrics** & \multicolumn{2}{c}{**Multimodal Model (Integrated All 3 Datasets)**} \\ \cline{2-3}  & **Old Value** & **New Value** \\ \hline Accuracy & 0.60 & 0.63 \\ Precision & 0.55 & 0.59 \\ Recall & 1.00 & 0.90 \\ F1-Score & 0.71 & 0.71 \\ ROC–AUC & 0.60 & 0.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Cross-validation performance of multimodal model (integrated) using hyperparameter tuning. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Component** & **Hyperparameters** & **Values** \\ \hline \multirow{4}{*}{ResRNN-Net} & Bi-LSTM & Units & 64 \\  & CNN & Filters/Kernel Size & 64/3 \\  & Attention & Type & Additive Attention \\  & Dense Layer & Units/Activation & 128/ReLU \\  & Optimizer & Type/LR/BatchNormSize & Adam/0.0005/64 \\ \hline \multirow{4}{*}{MultiScaleFusion-Net} & Conv1D & Kernel Size & 1,3,5 \\  & BiGRU & Units & 64 \\  & Dense Layers & Units & 128\(\times\) 2 layers \\  & Fusion & Method & Concatenation \\ \hline \multirow{4}{*}{Multimodal Model} & CNN & Input Size/Activation & 224\(\times\) 224/ReLU \\  & LSTM & Units/Sequence Length & 64/Variable \\ \cline{1-1}  & MLP & Layers/Activation & 21 ayers (64,32)/ReLU \\ \cline{1-1}  & Fusion & Strategy & Concatenation + Dense Layer \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of key model hyperparameters. ### Performance Interpretation for ResRNN and MSF Net

The confusion matrix and ROC curve analyses present interesting observations of the efficacy and reliability of ResRNN-Net as well as MultiScaleFusion-Net to predict pregnancy complications. The first important observation is the high recall by both the models, guaranteeing that as many actual complication cases as possible were labeled correctly. The high sensitivity in this context is essential in real-world clinical uses, where the failure to recognize a potential complication would have a negative consequence. Figure 16 illustrates that the optimal performance for ResRNN-Net was at a threshold of 0.40, whereas the best results for MultiScaleFusion-Net, as shown in Figure 17, were obtained at a marginally increased threshold of 0.45. This variation indicates the need for threshold optimization to realize clinically meaningful predictions. Finally, the collective evidence from the confusion matrix and ROC curve analyses corroborated the models' performance, proving their feasibility for real-world application in maternal health tracking and early complication identification.