# Deep Recurrent Neural Network Assisted Stress Detection System for Working Professionals
## 4 Results and Discussion  
In the end, the concatenated output is produced as the output, and it provides the classified label indicating whether working professionals experience stress or do not experience stress. The following is a list of the primary benefits that result from conducting this work:  
1. Simple design;
2. Accurate prediction of results;
3. Reduced time consumption for training and testing models;
4. Better performance rate.  
This section explores the outcomes of the existing and proposed techniques in terms of various evaluation metrics, namely, _Accuracy_, _Precision_, _Recall_, _Sensitivity_, _Specificity_, _F\(1\)-_Score_, and _Error Rate_.  
The figures are calculated as follows:  
\[Sensitivity=\frac{TP}{TP+FN} \tag{7}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{8}\]  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
\[F1\_Score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{11}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{12}\]  
\[Error\_Rate=1-Accuracy \tag{13}\]  
_TP_ stands for True Positives, _TN_ stands for True Negatives, _FP_ stands for False Positives, and _FN_ stands for False Negatives. The _Accuracy_, _Precision_, and _Recall_ measures of the existing and proposed classification techniques are shown in Table 1 and Figure 5. As a result, the _RMSE_ and _F\(1\)-Score_ of the existing techniques, namely, ANN, LR, NB, SVM, and DT, and the proposed classification technique are shown in Table 2 and Figure 6. Typically, the performance of a prediction system is determined by these factors, with the number of features/attributes used to train the data model having a significant impact on the accuracy of a working professional's stress prediction. The proposed DRNN technique outperforms the other techniques in terms of _Accuracy_, _Precision_, _Recall_, _F\(1\)-Score_, and _RMSE_ values, as evidenced by the results. This demonstrates that the proposed DRNN-based working professional stress prediction technique outperforms other techniques in terms of overall performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Techniques** & _Accuracy_ & _Precision_ & _Recall_ \\ \hline ANN & 77 & 83 & 93 \\ LR & 75 & 81 & 92 \\ NB & 78 & 84 & 92 \\ SVM & 78 & 82 & 93 \\ DT & 71 & 81 & 85 \\ Proposed DRNN & 98.56 & 99.32 & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy, precision, and recall measures of existing and proposed techniques.  
Furthermore, the prediction performance of the existing SVM and ANN techniques and the proposed DRNN technique is assessed using a variety of working professionals' sessions, as shown in Figure 7 and Table 3. Based on these findings, it is determined that the proposed DRNN technique outperforms the existing techniques because, in the proposed system, the working professionals' stress is predicted before and after work, as well as their participation in all of the institution's events. Additionally, the working professionals' feedback is collected and updated with the dataset, in which the most appropriate features for improving classification performance are extracted.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Techniques** & **F1-Score** & RMSE \\ \hline ANN & 87 & 50 \\ LR & 86 & 52 \\ NB & 87 & 51 \\ SVM & 85.23 & 49.27 \\ DT & 82 & 54 \\ Proposed DRNN & 98.47 & 32.89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: F1-scores and RMSE measures of existing and proposed techniques.  
Figure 5: Accuracy, precision, and recall of existing and proposed techniques.  
Figure 6: RMSE and F1-score of both existing and proposed techniques.