## 4 Results
The baseline ResNet model achieved an AUC of 0.844. On the other hand, Figure 6b demonstrates the MCC performance of the models. The ResNet model achieves an MCC score of approximately 52.77%. When ResNet is combined with Grad-CAM-based feature selection (FS) and traditional machine learning classifiers such as SVM, XGBoost, AdaBoost, and RF, there are minor performance improvements. Among these algorithms, RF achieves the highest MCC, which is 53.98%. The most significant improvement is observed with the combination of ResNet + Grad-CAM + FS + BO-DNN, which achieves the highest MCC score of 59.10%. This demonstrates a substantial improvement over both the baseline ResNet model and other hybrid configurations. The results highlight the effectiveness of using Grad-CAM feature extraction with the Bayesian optimization approach (BO-DNN) in combination with feature selection to enhance predictive performance. On the other hand, Figure 7 presents a radar chart that visualizes and compares the performance of all models in terms of performance metrics such as accuracy, AUC, precision, recall, F1-score, and MCC. The significance of this chart lies in its ability to provide an overall view of model performance by covering multiple metrics in a single visual representation. Each metric offers a unique perspective on the capabilities of the model. For instance, AUC and accuracy represent the overall classification ability, precision and recall highlight the balance between false positives and false negatives, F1-score captures the harmonic mean of precision and recall, and MCC evaluates the quality of binary classifications, especially for imbalanced datasets. The ResNet + Grad-CAM + FS + BO-DNN model stands out with the largest area in the radar chart, indicating superior performance in terms of metrics, indicating its strong generalization capability. This demonstrates that Bayesian optimization effectively tunes the deep neural network to learn robust features from the data. On the other hand, other configurations, such as ResNet + Grad-CAM + FS + RF, show competitive results in metrics like AUC and precision. However, the smaller overall area for these models suggests that their performance is less consistent across all metrics, highlighting potential trade-offs or limitations in certain  
Figure 7: Radar chart for the performance of models in terms of accuracy, F1-score, recall, precision, MCC, and AUC metrics. aspects, such as recall and MCC. By combining these metrics, Figure 7 highlights the importance of balancing performance across multiple dimensions rather than optimizing for a single metric. Figure 8 indicates the receiver operating characteristic (ROC) curves for the models employed in this study to classify bipolar and SCH disorders. As seen in the figure, the ResNet + Grad-CAM + FS + BO-DNN model achieves the highest mean AUC (0.83 \(\pm\) 0.10), indicating its relatively better performance among the six models. On the other hand, AdaBoost and Random Forest demonstrate comparable performance to that of the BO-DNN model. SVM, XGBoost, and the baseline ResNet model achieve slightly lower AUC values, with minor differences among them. Moreover, the DL model demonstrates superior performance, especially in the lower false positive rate range (FPR: 0.0-0.2), as observed in its ROC curve. AdaBoost and Random Forest also exhibit consistent performance across all FPR values, demonstrating their effectiveness for this task. Table 2 presents the comparison of various models in terms of AUC values provided in [31] and the models proposed in this study. In [31], the authors employed traditional methods such as 1D CNN, logistic regression (LR), support vector machine (SVM), linear discriminant analysis (LDA), Gaussian naive Bayes (GNB), k-nearest neighbors (KNN), decision tree (DT), and random forest (RF), and submitted their study to the IEEE Signal Processing Cup (SPC) 2023. As seen in the table, traditional methods generally exhibit moderate AUC values. They presented their results as public and private AUC values.