## 4 Results  
This article aims to propose two XAI-based models for use in monitoring the MMR risk in a certain region in a country and at the patient level by ensuring that the patient's information is sent to the medical reception through an efficient communication system. The following subsections represent the experiments and the results for each part. Region-level data results:  
To propose an XAI model, extensive assessment of the available features is required, as this helps understand the relationships among the features and their effect on the results. The parameters of the region-level dataset were first analyzed by checking the Pearson correlation of the 33 parameters, as shown in Figure 5. The figure shows that all the features have a low-to-average correlation with the class MMR. There is a high correlation between \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Feature Name** & **Description** & **Mean** & **Std** & **Min** & **0.25** & **0.50** & **0.75** & **Max** \\ \hline Age & Patient age & 29.87 & 13.47 & 10.00 & 19.00 & 26.00 & 39.00 & 70.00 \\ \hline SystolicBP & Systolic blood pressure & 113.20 & 18.40 & 70.00 & 100.00 & 120.00 & 120.00 & 160.00 \\ \hline DiastolicBP & Diastolic blood pressure & 76.46 & 13.89 & 49.00 & 65.00 & 80.00 & 90.00 & 100.00 \\ \hline BS & Blood sugar level & 8.73 & 3.29 & 6.00 & 6.90 & 7.50 & 8.00 & 19.00 \\ \hline BodyTemp & Body temperature & 98.67 & 1.37 & 98.00 & 98.00 & 98.00 & 98.00 & 103.00 \\ \hline HeartRate & Heart rate & 74.30 & 8.09 & 7.00 & 70.00 & 76.00 & 80.00 & 90.00 \\ \hline RiskLevel & MMR risk level & 0.87 & 0.81 & 0.00 & 0.00 & 1.00 & 2.00 & 2.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The parameters of the patient-based MMR assessment dataset. some of the features and others, which helps physicians better understand the relations among the different factors. To evaluate the performance of different models in classifying state risk, several experiments were conducted using the entire dataset and 10-fold cross-validation. A variety of machine learning methods were employed, like decision tree (DT), logistic regression (LR), support vector classifier (SVC), and multi-layer perceptron (MLP) versus the proposed CNN-GRU model. The DT model employed the Gini index according to the best split. The LR model used was the default, which attempts to calculate an intercept term. The SVC model uses the radial basis function (RBF) kernel and a regularization parameter C of 1.0. The results, summarized in Table 4, demonstrate the superiority of the proposed CNN-GRU deep learning model. The MLP has a single hidden layer with 100 neurons, employing the ReLU activation function, and is trained with the Adam optimizer. It consistently achieves higher accuracy, precision, recall, F1-score, and AUC compared to all the other models considered. The proposed model was trained for 200 epochs, as shown in Figure 6. By analyzing the charts it can be noted that there was no further improvement expected after epoch 200 and the shape of the curves suggests that the presented model did not overfit using the data at hand. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline DT & 0.857 & 0.858 & 0.857 & 0.857 & 0.970 \\ \hline LR & 0.896 & 0.899 & 0.896 & 0.896 & 0.969 \\ \hline SVC & 0.881 & 0.884 & 0.881 & 0.880 & 0.967 \\ \hline MLP & 0.815 & 0.844 & 0.815 & 0.812 & 0.969 \\ \hline Proposed & 0.926 & 0.926 & 0.926 & 0.926 & 0.970 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Different assessment results for various models on the region dataset. Figure 5: Pearson correlation of the region-based MMR assessment using all 33 features. The naming labels denote only the odd features from the list. Figure 7 shows the SHAP summary for the two classes addressed in the region dataset. The feature influence on the low-risk class is shown in Figure 7a and the high-risk in Figure 7b.