## 4 Experimental Results and Its Analysis in Real-Time Environment
### Real-Time Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**S.no**} & \multirow{2}{*}{**Accuracy Measures**} & **CNN-LSTM** & **CNN** & **Bi-LSTM** & **Dense-NN** & **Proposed** \\  & & [26] & [27] & [1] & [1] & **Method** \\ \hline
1. & Accuracy & 93 & 96 & 95 & 90 & 98.85 \\
2. & Precision & 0.93 & 0.97 & 0.97 & 0.92 & 0.98 \\
3. & Recall & 0.93 & 0.96 & 0.86 & 0.87 & 0.94 \\
4. & F1-score & 0.92 & 0.96 & 0.92 & 0.90 & 0.96 \\
5. & Cohen’s Kappa Score & 0.95 & 0.95 & 0.89 & 0.89 & 0.94 \\
6. & ROC-Accuracy & 0.96 & 0.96 & 0.99 & 0.95 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: A Comparative Study of various Deep Learning Algorithms for the Classification of Diabetes using 10-fold Cross-Validation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**S.no**} & \multirow{2}{*}{**Accuracy Measures**} & **CNN-LSTM** & **CNN** & **Bi-LSTM** & **Dense-NN** & **Proposed** \\  & & [26] & [27] & [1] & [1] & **Method** \\ \hline
1. & Accuracy & 90 & 82 & 85 & 87 & 88.37 \\
2. & Precision & 0.84 & 0.72 & 0.82 & 0.84 & 0.85 \\
3. & Recall & 0.79 & 0.77 & 0.70 & 0.75 & 0.79 \\
4. & F1-score & 0.83 & 0.74 & 0.75 & 0.79 & 0.81 \\
5. & Cohen’s Kappa Score & 0.76 & 0.61 & 0.64 & 0.70 & 0.73 \\
6. & ROC-Accuracy & 0.89 & 0.87 & 0.87 & 0.91 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative study of various deep learning algorithms for classification of siabetes by dividing dataset into testing and training [59; 60]. The performance of different models can be visualized through a graph, as shown in Figure 12. When compared to other current approaches, it is seen that a testing dataset is adequately fitted to the training model in CNN-Bi-LSTM with very few distortions. Hence, our suggested framework is more accurate and capable of properly classifying diabetic patients. Additionally, we have tested various CNN-Bi-LSTM findings over-optimized hyperparameters with different mean test scores (reference from Table 7), and the rest of the results of our proposed methodology with different mean test scores are presented in Table 13. It is clearly seen that our model outperformed, with the highest mean test score at 90 (Reference from Table 10). Lastly, comparisons were made between various state-of-the-art algorithms and the proposed model in terms of accuracy, as shown in Table 14, and it was found that CNN-Bi-LSTM outperformed in terms of accuracy (referenced from Table 10) [61; 62]. After all these conversations, we may infer that the CNN-Bi-LSTM model \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** \\ \hline
0 & 0.83 & 0.88 & 0.85 \\
1 & 0.75 & 0.65 & 0.70 \\ \hline Accuracy & & & 80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Validation of proposed model with K-fold cross-validation without filling missing values. Figure 11: (**a**) Model accuracy of CNN with 10-fold cross-validation. (**b**) Model accuracy of optimized proposed model (CNN-Bi-LSTM). (**c**) Model accuracy of CNN-LSTM model. (**d**) Model loss of CNN. (**e**) Model loss of proposed model. (**f**) Model loss of CNN-LSTM. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** \\ \hline
0 & 0.81 & 0.87 & 0.83 \\
1 & 0.72 & 0.61 & 0.70 \\ \hline Accuracy & & & 79\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Validation of proposed model with K-fold cross-validation without removing outlier values. is more accurate in identifying diabetes patients than the other four models as well as state-of-the-art algorithms. As a result of the fact that CNN-Bi-LSTM combines the power of both CNN and Bi-LSTM, where CNN is used for feature extraction, and Bi-LSTM has additional peehole connections that prevent vanishing gradient problems as well as additional cell state memory that uses past and future knowledge to forecast the output by using two separate hidden layers.