## 4 Proposed Epileptic Seizures Detection Model
This dual approach ensures that the most relevant features are retained, leading to a more efficient and accurate classification process. By systematically addressing feature redundancy and computational overhead, the proposed method offers a scalable and adaptable solution suitable for real-time seizure detection across diverse patient datasets. 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]. 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 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. While several studies have reported high classification accuracies using EEG signals, these results are often achieved under constrained or idealized conditions, such as balanced datasets, patient-specific models, or limited validation protocols. Such settings do not reflect real-world clinical environments, where seizure data is scarce, highly imbalanced, and heterogeneous across patients. Moreover, prior models tend to rely on either time domain or frequency domain features in isolation, which limits their ability to capture the full dynamics of EEG signals. This often results in overfitting and poor generalization of new, unseen data. Additionally, the use of imbalanced datasets without robust oversampling strategies reduces sensitivity to rare seizure events, failing to address the clinical priority of minimizing missed detection. In contrast, our proposed approach introduces a novel and holistic pipeline that addresses these persistent limitations. By integrating SMOTE solely on the training set, we generate synthetic minority-class instances in a way that avoids data leakage and enhances generalization. The hybrid use of PCA and DWT combines dimensionality reduction with detailed time-frequency feature extraction, enabling the model to capture subtle and complex patterns associated with seizures. This fusion of feature spaces provides a more discriminative and noise-resilient representation than either method alone. Finally, the use of SVM, known for its robustness in high-dimensional spaces, allows for effective classification even under challenging data conditions. Together, this SMOTE + PCA + DWT + SVM combination offers a novel, clinically relevant, and generalizable solution to the unresolved challenges in seizure detection--bridging the gap between research accuracy and real-world utility. Furthermore, this research makes a significant contribution by evaluating multiple ML classifiers (SVM, RF, DT, and KNN) to determine the optimal model for seizure prediction. Unlike prior studies that focus on patient-specific models, which often lack generalizability, this work emphasizes the development of a robust, patient-independent detection system. The proposed approach incorporates advanced data preprocessing techniques, including outlier removal and dataset balancing, with the Synthetic Minority Over-Sampling Technique (SMOTE), to mitigate bias in training models. By streamlining the detection process and improving diagnostic reliability, this work represents a substantial advancement in EEG-based seizure detection, with the potential for real-world clinical deployment and integration into wearable seizure monitoring systems.