# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
## 3 Methods Used
### Proposed Method
Confusion matrix of Figure 10a-f represents the result of classification (depression and non-depression) using different models: SVR, decision tree, linear regression, ridge regression, logic regression, and KNN, respectively. It can be inferred that all six models perform similarly in classifying depression severity, with similar numbers of observations being misclassified as different classes. Figure 7: ROC for proposed model and various CNN models for classification of the type of depression. #### 5.2.3 ROC of Various Classification Models

The ROC of various classification models are shown in Figure 11. The ROC of the proposed model is 99.5% and the ridge regression has an accuracy of 97.7%; the highest among all others comparison algorithms and the lowest being 95.0% for KNN model. After which, it can be observed that linear regression has an accuracy of 97.3%. Then, we found decision tree with 96.9% and logical regression with 96.2%, providing us with a very similar performance along with KNN model with 95.6% accuracy. Figure 8: Error comparison with machine learning algorithms. Figure 9: Confusion Matrix of Proposed Methodology. #### 5.2.4 Comparison of Proposed Model with Other Published Model of Depression Detection

We compared the performance of the proposed system to a number of different current schemes for depression detection. We specifically compared our proposed approach to those put forth by [PERSON] et al. [55], [PERSON] et al. [21], [PERSON] et al. [22], [PERSON] et al. [23], and [PERSON] et al. [7]. We measured each scheme's performance at 84.7, 91.76, 88.7, 86.65, and 69.62, respectively, while our proposed model performance is 98.70. These findings are summarized in Table 4. Our investigation demonstrates that our suggested system outperforms when compared to others published work for solving the same problems. It achieved a maximum training accuracy of 99.5% and 98.7% as testing accuracy. In Figure 12, it is clearly depicted from the all observation that the proposed model of deep learning gives comparatively good results as compared to other popular models of deep learning and machine learning for depression disease classification. For the dataset used, ridge and linear regression also given good result compared to the others. The proposed methodology after further hyper parameter tuning to improve the model's generalization ability performed best among the rest of the models with training accuracy of 99.5%. The second best to it is ridge regression with an accuracy of 98.75%, while the LSTM-based models are performing with low accuracies. Figure 10: (**a–f**) Confusion Matrix for models SVR, decision tree, linear regression, ridge regression, logic regression, and KNN. Figure 4:0 ROC for various models used for classification of the type of depression. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Type of Algorithm** & **Training Accuracy** & **Testing Accuracy** & **F1 Score** & **Recall** & **Precision** \\ \hline Proposed Model (PSCS) & 99.5 & 98.7 & 97.98 & 96.7 & 98.57 \\ \hline [PERSON] et al. (LSTM) [55] & 85 & 84.7 & 82.94 & 81.84 & 83.59 \\ \hline [PERSON] et al. (LSTM and CNN) [21] & 92.06 & 91.76 & 90.51 & 89.75 & 91.09 \\ \hline [PERSON] et al. (CNN and BiLSTM) [22] & 89 & 88.7 & 87.63 & 87.21 & 88.2 \\ \hline [PERSON] et al. (BiLSTM) [23] & 87.17 & 86.87 & 85.17 & 84.09 & 85.75 \\ \hline [PERSON] et al. (LSTM) [7] & 69.92 & 69.62 & 68.63 & 68.34 & 69.62 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance comparison among the various published models with proposed model for depression detection. ## 6 Conclusions

In this research, a novel machine learning approach was implemented in the data collected from Push Shift API and test sensors that explore the behavior of depression and the activity of various people. The number of the impact on social and professional functionality, as well as symptoms, contributes to the severity of depression. Another serious mental disorder is associated with depression.