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Epilepsy is a prevalent neurological disorder characterized by recurrent andunpredictable seizures, necessitating accurate prediction for effectivemanagement and patient care. Application of machine learning (ML) onelectroencephalogram (EEG) recordings, along with its ability to providevaluable insights into brain activity during seizures, is able to make accurateand robust seizure prediction an indispensable component in relevant studies.In this research, we present a comprehensive comparative analysis of fivemachine learning models - Random Forest (RF), Decision Tree (DT), Extra Trees(ET), Logistic Regression (LR), and Gradient Boosting (GB) - for the predictionof epileptic seizures using EEG data. The dataset underwent meticulouspreprocessing, including cleaning, normalization, outlier handling, andoversampling, ensuring data quality and facilitating accurate model training.These preprocessing techniques played a crucial role in enhancing the models'performance. The results of our analysis demonstrate the performance of eachmodel in terms of accuracy. The LR classifier achieved an accuracy of 56.95%,while GB and DT both attained 97.17% accuracy. RT achieved a higher accuracy of98.99%, while the ET model exhibited the best performance with an accuracy of99.29%. Our findings reveal that the ET model outperformed not only the othermodels in the comparative analysis but also surpassed the state-of-the-artresults from previous research. The superior performance of the ET model makesit a compelling choice for accurate and robust epileptic seizure predictionusing EEG data.

Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse Pre-Processing Techniques and Machine Learning Models