## 6 Experimental Results
The DT classifier displayed moderate performance with an AUC of 0.832, while KNN achieved an AUC of 0.885, indicating reasonable but comparatively weaker discrimination ability. A comparative ROC curve analysis of all classifiers (Figure 8) underscores the dominance of SVM and RF over DT and KNN in distinguishing between seizure and non-seizure events. The final performance comparison, visualized in Figure 9, consolidates these observations by illustrating the models' accuracy, AUC, and F1 score. The SVM and RF models consistently outperformed DT and KNN across all metrics. The superior performance of SVM can be attributed to its ability to find an optimal hyperplane that maximizes class separation, making it particularly effective for high-dimensional data such as EEG signals. RF, benefiting from its ensemble learning approach, successfully reduces overfitting and captures complex patterns, contributing to its high predictive power. In summary, the application of data balancing and hybrid feature selection techniques resulted in overall performance improvements, with SVM and RF emerging as the most effective models for seizure detection. DT showed moderate gains but remained less reliable, while KNN exhibited performance degradation, highlighting its limitations in handling high-dimensional, imbalanced data. These findings provide valuable insights into the selection of ML models for EEG-based seizure detection, with SVM and RF proving to be the most robust and efficient classifiers in this context. The evaluation of our proposed model reveals substantial advancements in seizure detection accuracy through EEG signal analysis. By integrating the SMOTE for data balancing and employing a hybrid feature selection approach combining PCA and DWT, our model demonstrates remarkable improvements across several algorithms, including SVM, RF, DT, and KNN. Figure 6: Confusion matrices for the evaluated classifiers (SVM, Random Forest, Decision Tree, and KNN). Rows represent true classes, and columns represent predicted classes. Class 0 corresponds to non-seizure activity, and Class 1 represents seizure events. Figure 7: ROC for each Classifier. Figure 8: Comparison of ROC Curves for all Classifiers. Figure 9: Performance Metrics Comparison Across Models. Our proposed model demonstrates significant performance improvements in seizure detection through the integration of SMOTE and hybrid feature selection techniques. The SVM and Random Forest models achieved the highest accuracy (97.30% and 97.17%, respectively) and near-perfect AUC scores (0.996), showcasing their superior classification capabilities. The Decision Tree model exhibited moderate improvements, while the KNN model experienced slight declines in accuracy and F1 score, indicating challenges with high-dimensional data. The ROC curve analysis confirmed the robustness of SVM and Random Forest, with high true positive rates and minimal false positives. These findings highlight the effectiveness of our approach in enhancing EEG signal analysis and seizure detection, setting a new benchmark for future research and clinical applications. Our proposed model's performance has been benchmarked against a range of recent studies using the Bonn (UCI) EEG dataset, as shown in Table 6. The results demonstrate meaningful improvements in both classification accuracy and recall, validating the effectiveness of our hybrid approach. [PERSON] and [PERSON] [32] developed an EEG seizure detection method based on time-frequency entropy and an LSSVM classifier, achieving 86% accuracy and 68% recall. Similarly, [PERSON] et al. [33] employed optimized DWT-based features for seizure localization, reporting 88% accuracy and 91.52% recall. In contrast, our model integrates both DWT and PCA for a richer feature representation, improving overall accuracy to 97.3%. More recent studies have aimed to improve performance using various classifiers and preprocessing strategies. [PERSON] et al.