## 4 Experimental Results and Its Analysis in Real-Time Environment
### Real-Time Quantitative Analysis  
For quantitative evaluation, we conducted a comparative analysis of deep learning models such as CNN [56], DNN [54], Bi-LSTM [55], CNN-LSTM [50], and CNN-Bi-LSTM, which are trained over a static PIDD dataset by splitting it into two portions, 70% for the training dataset and 30% for the testing dataset, by using hyperparameters such as kernel size as 1; the number of filters as 64; batch-size as 32; regularization dropout as 0.05; optimizer as; maximum pool size as 1; loss-method as binary cross-entropy; epsilon \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Learning Rate** & **Batch Size** & **Hidden Units** & **Epochs** & **Mean Test Score** \\ \hline
0.01 & 32 & 32 & 250 & 90.38 \\
0.03 & 64 & 64 & 300 & 85.58 \\
0.05 & 128 & 128 & 500 & 79.49 \\
0.09 & 64 & 64 & 300 & 89.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Optimized Hyper parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**S.no** & **Parameters** & **Values** \\ \hline
1. & Regularization (dropouts) & 0.05 \\
2. & Loss-function & Binary-crossentropy \\
3. & Optimizer & Adam \\
4. & Metrics & accuracy \\
5. & Learning Rate & 0.01 \\
6. & Batch-size & 32 \\
7. & Epochs & 250 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Optimized Parameters used for training. as 1\(-\)08; decay as 0.0; epochs as 250 with 32 hidden units and found CNN-LSTM [52] with 90%, CNN [51; 55; 57; 58] has an accuracy of 82%, Bi-LSTM [1] with 85%, DNN [1] with 87%, and, ultimately, CNN-Bi-LSTM outperforms and achieves an accuracy of 88.37% as shown in Table 9. Although the accuracy of all models is reasonable, they suffer from under-fitting and over-fitting problems, and they emerge when models have learned less than or more than 250 epochs. The over-fitting problem model tends to memorize data and cannot generalize new data, while the under-fitting model does poorly in testing but can generalize new data. To remove the over-fitting and under-fitting problems, we trained our models at 250 epochs and over-optimized parameters using 10-fold cross-validation, and the accuracy of each model was increased, as shown in Table 10. Besides, the accuracy of CNN-LSTM was increased to 93%, CNN was increased to 96%, BI-LSTM was also increased to 95%, and DNN was increased to 90%. However, our CNN-Bi-LSTM model outperformed compared to other models and achieved the highest accuracy of 98.85%, as shown in Figure 11, with a sensitivity of 97% and specificity of 98%. Thus, after the discussion, we can infer that CNN-Bi-LSTM is better relative to other deep learning models in terms of accuracy, sensitivity, specificity, precision, and recall. Therefore, we utilized the CNN-Bi-LSTM model in a real-time setting to classify diabetic patients more accurately as well as to monitor their vitals on a real-time basis. Additionally, we have validated our proposed model with different scenarios. First, the proposed model is validated without imputations where the values for precision-0.83, recall-0.88, and F1-score-0.85 for the outcome 0. However, for the outcome 1 values of precision-0.83, recall-0.88, F1-score-0.85 and accuracy-80% as shown in Table 11. Secondly, the proposed model is validated without removing outliers, where the values for precision-0.81, recall-0.87, and F1-score-0.83 for the outcome 0. However, for the outcome 1 values of precision-0.72, recall-0.61, F1-score-0.70 and accuracy-79% as shown in Table 12. Hence, missing values and outliers affect the performance of the model. Therefore, it is essential to preprocess data before training the proposed model.