## 3 Architectural System Design
### System Architecture
#### 3.8.1 ResRNN-Net and MultiScaleFusion-Net
* **Hardware Specifications:*
* All the experiments were conducted using the following hardware:
* **Programming Language:*
* Python 3. * Intel Xeon @ 2.00 GHz. * Google Colab. ### Explainable AI and Feature Importance

For transparency and trust in the pregnancy complication prediction models, explainable AI (XAI) methods were utilized, with SHapley Additive exPlanation (SHAP) being the main focus. This method provided both global feature importance insights and local explanations for each prediction, increasing the interpretability of the ResRNN-Net and MultiScaleFusion-Net models. To provide deeper interpretability of the SHAP visualizations, it is essential to understand what each feature index represents. Figure 18 presents the complete mapping of the SHAP feature indices to their corresponding clinical, demographic, and lifestyle variables in the NFHS-5 dataset. This mapping aids clinicians and researchers in translating model insights into actionable factors for maternal health evaluation. The SHAP summary plot in Figure 19 depicts the contribution of every feature regarding model predictions. For ResRNN-Net (including 10 samples), the major features were **Feature 47 (Water-Source-Other)**, **Feature 33 (Postnatal-check)**, **Feature 46 (Water-Source-Natural-Source)**, and **Feature 38 (Smoke)**, as presented by the summary plot. Positive values of SHAP represented features contributing to increased pregnancy complication prediction probabilities, whereas negative values decreased the prediction probability. **Feature 17 (Child-putToBrst)** and **Feature 21 (Child-Brst)** had significant contributions, which coincided with the clinical expectations. Figure 18: Feature mapping and importance. Figure 20 presents the Tabular-SHAP analysis for the multimodal model, which reveals that Feature 39 (Prenatal_Care) proves to be a prominent feature, although Feature 27 (Hg_child_adjust) and Feature 24 (Preg_intParaDrug) contribute notably. The SHAP feature importance bar plot as shown in Figure 21 also calculates the mean contribution of every feature regarding the model predictions. Features such as **47**, **33**,

Figure 19: SHAP plot: ResRnn. Figure 20: SHAP plot: multimodal [tabular]. and **46** recorded the highest mean SHAP values, justifying their critical role in classification. The visualization shows a more significant ordering of feature impact in the direction of decision interpretability by ResRNN-Net. These findings validate the model's adherence to the clinical expectations and its trustworthiness in practice. The MultiScaleFusion-Net SHAP feature importance plot in Figure 22 reveals Feature **39 (Prenatal_Care)** to be the most influential on the model predictions, with a much greater mean SHAP value than the others. Features **16 (ChildFood_bottle)**, **26 (ChildAge_mnnts)**, and **33 (PostnatalChk)** also make strong contributions, confirming their contribution to pregnancy complication classification. The distribution shows that MultiScaleFusion-Net is founded on a more specific collection of predictive variables than ResRNN-Net. In order to better understand the individual predictions, SHAP waterfall plots were created for example predictions of the two models. These plots decomposed the contri

Figure 21: SHAP summary for ResRNN-Net. Figure 22: SHAP summary for MultiScale Fusion Net. butions of each feature, illustrating how the model arrived at its final prediction. For ResRNN-Net, Figure 23 identified **Feature 39 (Prenatal_Care)** as the most contributory factor in reducing the prediction probability greatly. On the other hand, **Feature 22 (Preg_iron)** and others improved the likelihood of a positive prediction. The SHAP values cumulatively accounted for the last output, supporting the decision-making process of the model. Likewise, the waterfall plot of MultiScaleFusion-Net, shown in Figure 24, identified **Feature 39 (Prenatal_Care)** and **Feature 33 (PostnatalChk)** as major contributors to the prediction results.