## 4 Proposed Epileptic Seizures Detection Model  
Existing approaches relying on the visual inspection of EEG recordings by medical professionals often lead to delayed identification of early seizure indicators. This can result in subjective assessments and the potential for oversight [11]. Therefore, this paper aims to revolutionize the detection of epileptic seizures by integrating advanced ML methodologies. Epileptic seizures, characterized by erratic brain activity, pose a significant health concern requiring accurate and timely identification for effective intervention. To address this challenge, this research develops an automated and objective seizure detection system using sophisticated ML techniques. The goal is to substantially improve the accuracy and timeliness of seizure detection compared with conventional methods. The significance of this research lies in its potential to greatly enhance the quality of life for individuals with epilepsy by reducing the risks associated with delayed or inaccurate seizure detection. Leveraging ML models holds the promise of improving diagnostic accuracy while streamlining the detection process. The proposed model tackles the issue of \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Citation** & **SVM** & **PCA** & **DWT** & **RF** & **MLPNN PE** & **NB** & **KNN** & **GWO** & **ELM** \\ \hline [PERSON] [13] & ✓ & - & - & - & - & - & - & - & - & - \\ \hline [PERSON] et al. [14] & - & ✓ & - & - & - & - & - & - & - & - \\ \hline [PERSON] et al. [15] & - & - & - & ✓ & - & - & - & - & - & - \\ \hline [PERSON] and [PERSON] [16] & - & ✓ & ✓ & ✓ & - & - & - & - & - & - \\ \hline [PERSON] and [PERSON] [17] & - & ✓ & - & - & - & - & - & - & - & - \\ \hline [PERSON] et al. [19] & - & - & ✓ & - & ✓ & - & - & - & - & - \\ \hline [PERSON] and [PERSON] [20] & - & - & - & - & - & ✓ & - & - & - & - \\ \hline [PERSON] et al. [21] & ✓ & - & ✓ & - & - & - & - & - & - & - \\ \hline [PERSON] and [PERSON] [7] & - & - & ✓ & - & - & - & ✓ & ✓ & - & - \\ \hline [PERSON] et al. [22] & ✓ & - & - & - & - & - & - & - & ✓ & - \\ \hline [PERSON] and [PERSON] [23] & - & - & - & - & - & - & - & - & - & ✓ \\ \hline [PERSON] et al. [24] & ✓ & ✓ & - & - & ✓ & - & - & - & - & - \\ \hline [PERSON] et al. [25] & ✓ & - & - & - & - & - & - & ✓ & - & - \\ \hline [PERSON] et al. [26] & - & - & - & ✓ & - & - & - & - & - & - \\ \hline [PERSON] and [PERSON] [27] & ✓ & - & - & - & - & - & - & - & - & - \\ \hline [PERSON] and [PERSON] [28] & - & - & - & ✓ & - & - & - & - & - & - \\ \hline [PERSON] et al. [29] & - & - & - & - & ✓ & - & - & - & - & - \\ \hline [PERSON] et al. [30] & - & - & ✓ & - & ✓ & - & - & - & - & - \\ \hline [PERSON] et al. [31] & - & ✓ & - & - & - & - & ✓ & - & - & - \\ \hline [PERSON] and [PERSON] [32] & ✓ & - & - & - & - & - & - & - & - & - \\ \hline [PERSON] et al. [33] & ✓ & - & ✓ & - & - & - & - & - & - & - \\ \hline [PERSON] et al. [34] & - & - & - & ✓ & ✓ & - & - & - & - & - \\ \hline [PERSON] et al. [35] & ✓ & - & - & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature Selection Techniques used in related papers. accurate epileptic seizure prediction through the integration of effective feature selection techniques and classification methods. This involves enhancing prediction accuracy while minimizing false detections. To achieve this, we employ PCA and DWT as hybrid feature selection methods, chosen for their demonstrated effectiveness across various applications. The integration of PCA and DWT as a hybrid feature selection technique enhances the discriminatory power of extracted features for epileptic seizure detection. Unlike traditional methods that either rely on time domain or frequency domain analysis separately, this approach effectively captures both spatial and temporal patterns in EEG signals. PCA reduces dimensionality while preserving essential seizure-related information, whereas DWT decomposes EEG signals into multiple frequency sub-bands to identify seizure-specific characteristics.