## 4 Experimental Results and Its Analysis in Real-Time Environment
### Real-Time Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S.no** & **Author** & **Dataset** & **Validation Technique** & **Algorithm Used** & **Accuracy** \\ \hline
1. & [PERSON] (2017) et al. (2017) & PIMA Dataset & 0.1 split validation & Deep learning architecture & 88.41\% \\
2. & [PERSON] et al. (2015) & PIMA Dataset & Divide hyperplane & SVM & 77\% \\
3. & [PERSON] (2019) et al. (2019) et al. (2019) & PIMA Dataset & 70\% Training, 30\% Testing & DNN with auto-encoders & 86\% \\
4. & [PERSON] (2019) et al. (2019) et al. (2019) & PIMA Dataset & The training and validation sets are split in an 80/20 ratio & LSTM & 86\% \\
5. & [PERSON] et al. (2014) & PIMA Dataset & K-fold cross-validation & PCA, K-Means Algorithm & 73\% \\
6. & [PERSON] and [PERSON] (2016) et al. (2016) et al. (2019) & PIMA Dataset & 70\% Training, 30\% Testing & SVM and NN & 96.09\% \\
7. & [PERSON] and [PERSON] (2019) & PIMA Dataset & 70\% Training, 30\% Testing & RF & 94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of our model with other state-of-the-art algorithms in terms of accuracy mentioned in related work. Figure 12: Performance analysis of optimized proposed methodology with respect to existing deep learning algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S.no** & **Accuracy** & **Mean-Test** & **Mean-Test** & **Mean-Test** \\
**Measures** & **Score 85.58\%** & **Score 79.49\%** & **Score 89\%** \\ \hline
1. & Accuracy & 91 & 78 & 67 \\
2. & Precision & 0.87 & 0.67 & 0 \\
3. & Recall & 0.82 & 0.67 & 0 \\
4. & F1-Score & 0.85 & 0.67 & 0 \\
5. & Cohens Kappa & 0.78 & 0.51 & 0 \\
6. & ROC-Accuracy & 0.92 & 0.83 & 0.28 \\
7. & Sensitivity & 91\% & 84\% & 2 \\
8. & Specificity & 0.87\% & 67\% & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Results of the proposed architecture with different Mean Test Scores using Grid Search Optimization. In order to check the performance of deep learning models, we additionally performed a statistical Student t-test between CNN-LSTM and CNN_Bi-LSTM. We used the variance estimate by checking the dependency between the dataset and computing the \(p\)-value. In the null hypothesis, we assumed that there was no statistical difference between the performances of the models. However, the alternative hypothesis we have considered is that there is a potential difference between the performance of models. If the \(p\)-value is less than the significant value, then we rejected the null hypothesis and assumed that there was no significant difference between the performance of deep learning models. If the \(p\)-value was higher, then we rejected the hypothesis and considered the alternative hypothesis. In order to calculate the \(p\)-value, we calculated the mean of the difference between the results of two classifiers at every iteration step as we are using K-fold cross-validation by using the formula: \(\overline{d}=\frac{1}{n}\sum_{1}^{n}d_{i}\). Then we calculated the variance of the difference, through \(\overline{\sigma}^{2}=\frac{\sum_{1}^{n}\left(d_{i}-\overline{d}\right)^{2}}{ n-1}\) where \(n\) is the total number of data points. Then we computed the data points used for training, i.e., \(n_{1}\) and data points for testing \(n_{2}\). Then we computed the mod of variance, \(\overline{\sigma}^{2}{}_{Mod}=\left(\frac{1}{n_{1}}+\frac{n_{1}}{n_{2}} \right)\overline{\sigma}^{2}\) and finally, we calculate the time statics through \(\overline{\sigma}_{Mod}\). The calculation of the \(p\)-value is shown in Figure 13, where it can be easily seen that the \(p\)-value is approximately 1.84%, and the significance value is 5%. As the \(p\)-value is less than the significance value, we rejected the null hypothesis and assume that the performance of the proposed model is different from others and better in terms of accuracy as compared to other models (referenced from Table 10).