## 6 Experimental Results  
The outcomes derived from employing four separate algorithms, SVM, DT, RF, and KNN, have been meticulously recorded. The performance metrics include accuracy, AUC, F1 score, and the average of these metrics. The results are summarized in Tables 4 and 5. The application of data balancing and hybrid feature selection (PCA + DWT) techniques generally improved the performance of all four classifiers. The most notable improvements were observed in the SVM and RF models. Before the application of data balancing and hybrid feature selection techniques, the SVM and RF models already demonstrated strong classification capabilities, with SVM achieving an accuracy of 97.00% and an AUC of 99.40%, while RF followed closely with an accuracy of 96.30% and an AUC of 99.20%. In contrast, the DT and KNN models exhibited relatively lower performance, with DT showing an accuracy of 93.40% and an AUC of 81.50%, and KNN achieving an accuracy of 93.60% with an AUC of 90.30%. These initial results suggest that, while DT and KNN can serve as viable classifiers, they are less robust compared to SVM and RF. After implementing data balancing and feature selection techniques, noticeable improvements were observed across most models. The SVM classifier experienced a marginal enhancement in all metrics, with accuracy increasing to 97.30%, AUC rising to 99.62%, and the F1 score improving from 92.30% to 93.08%. This indicates the model's high reliability in distinguishing between seizure and no seizure events. Similarly, RF showed significant \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **AUC** & **F1 Score** \\ \hline SVM & 97\% & 99.4\% & 92.3\% \\ \hline DT & 93.4\% & 81.50\% & 82.5\% \\ \hline RF & 96.3\% & 99.2\% & 90.2\% \\ \hline KNN & 93.6\% & 90.3\% & 80.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Initial Model Performance Without Application of Data Balancing. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **AUC** & **F1 Score** \\ \hline SVM & 97.3\% & 99.62\% & 93.08\% \\ \hline DT & 94.48\% & 83.24\% & 85.87\% \\ \hline RF & 97.17\% & 99.56\% & 92.72\% \\ \hline KNN & 92.13\% & 88.48\% & 76.03\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model Performance After Application of Data Balancing and Hybrid. gains, achieving an accuracy of 97.17%, an AUC of 99.56%, and an F1 score of 92.72%, reinforcing its strong predictive capability. The DT model demonstrated moderate improvements, with accuracy increasing from 93.40% to 94.48%, the AUC improving from 81.50% to 83.24%, and the F1 score rising from 82.50% to 85.87%. Despite these enhancements, DT continued to lag behind SVM and RF, suggesting potential overfitting issues and limitations in handling complex patterns. On the other hand, KNN displayed mixed results, with its accuracy declining from 93.60% to 92.13%, the AUC decreasing from 90.30% to 88.48%, and the F1 score dropping from 80.60% to 76.03%. This reduction in performance indicates that KNN may not be well-suited to high-dimensional data or imbalanced class distributions, highlighting its sensitivity to changes in feature selection and data balancing. Further evaluation was conducted through confusion matrices, providing insights into each model's classification errors and overall predictive accuracy. The confusion matrices, as illustrated in Figure 6, reveal that SVM and RF exhibit the lowest misclassification rates, further validating their superiority in seizure detection. The DT and KNN models, however, displayed a higher rate of false positives and false negatives, suggesting their relative inefficiency in precise classification. Additionally, the ROC curve analysis, as shown in Figure 7, further substantiates the findings. The SVM classifier exhibited an outstanding AUC of 0.996, positioning it as the best-performing model alongside RF, which also achieved an AUC of 0.996.