## 3 Architectural System Design
### System Architecture
#### 3.8.1 ResRNN-Net and MultiScaleFusion-Net
## 4 Results

To guarantee the generalization and robustness of the constructed deep learning models, ResRNN-Net, MultiScaleFusion-Net, and the multimodal model underwent a

\[\mathbf{Z}^{(l)}=\text{ReLU}\Bigg{(}\sum_{c=1}^{C}\mathbf{W}_{c}^{(l)}*\mathbf{ X}_{c}+b^{(l)}\Bigg{)}\]0 Extended proposed multimodal deep learning architecture. The system fuses three distinct input modalities: (i) ultrasound images processed through a CNN backbone, (ii) EHG time series processed with LSTM layers, and (iii) structured tabular data processed by a multi-layer perceptron (MLP). Learned feature vectors are concatenated and passed through fully connected layers for final binary classification, enabling comprehensive prediction by leveraging heterogeneous data sources. stringent cross-validation scheme. The multimodal model was evaluated separately in two experimental settings. * Training solely on tabular data (NFHS-5 dataset);
* Training on a fusion of tabular, image, and time-series data. ### Cross-Validation Strategy

The dataset was divided employing 5-fold stratified cross-validation such that every fold had an equal ratio of positive and negative pregnancy complications. This approach enabled deep learning models to be trained and tested on several data splits, minimizing variance and providing better estimation of model performance. For ResRNN and MultiScaleFusion-Net, in every fold, the data were split into 80% training and 20% validation subsets. Due to the heterogeneous nature of the datasets, special measures were taken for the images, the data were augmented to prevent overfitting, and, for the time series, the data were segmented and processed with padding to maintain sequence length consistency, and then a combination strategy was adopted where each fold contained a balanced subset of all three data types. #### 4.1.1 Model Training and Evaluation Using Cross-Validation

In both the folds, ResRNN-Net and MultiScaleFusion-Net were trained for 10 epochs with a batch size of 32. The models were validated on the validation set during training using the following key performance metrics: accuracy (overall accuracy of predictions), precision (proportion of true positive predictions out of all positive predictions), recall (capacity to spot true positive cases), F1-score (harmonic mean of precision and recall), and ROC-AUC (robustness over different thresholds). The performance metrics were estimated from the validation set using the evaluation function and contrasted against true labels, with each performance measure being computed. The performance metrics for the final model were obtained by averaging the results from all five folds. ResRNN-Net had an F1-score of 0.82, an ROC-AUC of 0.79, and an accuracy of 79%, which is indicative of excellent predictive power. Likewise, MultiScaleFusion-Net achieved an F1-score of 0.82, an ROC-AUC of 0.80, and an accuracy of 80%, which is very close to the performance of ResRNN-Net. A detailed comparison of the cross-validation results for both models is presented in \[\mathbf{Z}^{(l)}=\text{ReLU}\Bigg{(}\sum_{c=1}^{C}\mathbf{W}_{c}^{(l)}*\mathbf{ X}_{c}+b^{(l)}\Bigg{)}\]1

For the multimodal model, two experiments, as shown in Table 4, were observed and both were trained with 10 epochs, 32 as the batch size, BCE-Loss, and an Adam optimizer with a learning rate of 0.001. The tabular-only model achieved 80% accuracy with balanced performance across all the metrics, while the integrated multimodal model had higher recall (100%), indicating that it detected all the positive cases but at the cost of lower precision (55%). \begin{table}
\begin{tabular}{l c c} \hline
**Performance Metrics** & **ResRNN-Net** & **MultiScaleFusion-Net** \\ \hline Accuracy & 0.79 \(\pm\) 0.0022 & 0.80 \(\pm\) 0.0040 \\ Precision & 0.74 \(\pm\) 0.0033 & 0.74 \(\pm\) 0.0121 \\ Recall & 0.91 \(\pm\) 0.0100 & 0.91 \(\pm\) 0.0169 \\ F1-Score & 0.82 \(\pm\) 0.0039 & 0.82 \(\pm\) 0.0027 \\ ROC-AUC & 0.79 \(\pm\) 0.0017 & 0.80 \(\pm\) 0.0036 \\ \hline \end{tabular}
\end{table}