# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
## 3 Methods Used
### Proposed Method
#### 5.1.2 Precision and Recall

The precision-recall graphs shown in Figure 6a-d are often used metrics for assessing the effectiveness of classification models. Whereas recall represents the proportion of real positive predictions to all actual positive examples in the dataset, precision is the ratio of true positive predictions to all positive predictions made by the model. The best results of each model with a specific threshold value have been used for comparing the precision-recall curves of each model. Here amongst all other deep learning models (VGG, ResNet, LeNet) the precision-recall graph for our proposed model (PS-CS) shows slightly better results since the training and testing curves are narrower here. Figure 4: Accuracy and Loss graph graphs for the proposed model. Figure 5: Training and Validation loss comparison of deep learning models. #### 5.1.3 Receiver Operating Characteristic (ROC) Curve

A receiver operating characteristic (ROC) curve is a graphical depiction of the performance of a binary classifier when the classification threshold is changed. For various threshold values, it compares the true positive rate (TPR) against the false positive rate (FPR). Figure 7 compare the ROC curves for various deep learning models and proposed model, respectively. This can give us an idea of how well they perform in terms of classification. Part B: Comparison of Proposed Approach Performance with Classification Results of Other Popular Classification Models for Classification of Depressed vs Non-Depressed Patient

In this study, we performed a comparison of various popular classification models on the same dataset to ensure a fair evaluation. We used the same training and testing ratio for each model and ensured that all models used the same parameters. The models used for comparison were support vector regression (SVR), decision tree, linear regression, logistic regression, ridge regression, and K-nearest neighbors (KNNs). We applied the same evaluation metrics to each model to ensure a fair comparison. These metrics included R-squared error and mean squared error (MSE). The R-squared error quantifies the proportion of variance in the target variable that is predicted by the model; the MSE is a commonly used metric in machine learning for regression issues and offers a measure of how effectively the model is able to capture the variability of the data. Figure 6: Precision–Recall graph for VGG model (**a**), ResNet model (**b**), Proposed model (**c**), LeNet Model (**d**). #### 5.2.1. R-Squared and Mean Squared Error

The evaluation of models is essential to assess their performance in predicting outcomes. Two widely used metrics for evaluating model performance are R-squared error and mean squared error (MSE). R-squared error determines the fraction of variance in the predicted target variable by the model, while MSE measures the average squared difference between the predicted and actual values of the target variable. Figure 8 depicted that the proposed model has the lowest R-squared error of 0.02 and MSE of 0.25, indicating its superior predictive accuracy compared to the other models. The other models have relatively higher R-squared error and MSE compared to the proposed model. #### 5.2.2. Confusion Matrix of Various Classification Models

In Figure 9, the confusion of the proposed algorithm and Figure 10a-f are discussed. The confusion matrix of different comparison algorithms is used to evaluate the performance of a classification algorithm. It is used to describe the performance of a classification model by comparing the predicted class with the true class. It allows us to visualize the number of correct and incorrect predictions made by the model. The diagonal elements of the matrix represent the number of observations that were correctly classified, while the off-diagonal elements represent the number of observations that were incorrectly classified. By analyzing the confusion matrix, we can understand which classes the model is having difficulty with and make adjustments accordingly.