Comparative Analysis of Epileptic Seizure Prediction: Exploring Diverse
  Pre-Processing Techniques and Machine Learning Models
Md. Simul Hasan Talukder Rejwan Bin Sulaiman
Epilepsy is a prevalent neurological disorder characterized by recurrent and
unpredictable seizures, necessitating accurate prediction for effective
management and patient care. Application of machine learning (ML) on
electroencephalogram (EEG) recordings, along with its ability to provide
valuable insights into brain activity during seizures, is able to make accurate
and robust seizure prediction an indispensable component in relevant studies.
In this research, we present a comprehensive comparative analysis of five
machine learning models - Random Forest (RF), Decision Tree (DT), Extra Trees
(ET), Logistic Regression (LR), and Gradient Boosting (GB) - for the prediction
of epileptic seizures using EEG data. The dataset underwent meticulous
preprocessing, including cleaning, normalization, outlier handling, and
oversampling, 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 each
model 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 of
98.99%, while the ET model exhibited the best performance with an accuracy of
99.29%. Our findings reveal that the ET model outperformed not only the other
models in the comparative analysis but also surpassed the state-of-the-art
results from previous research. The superior performance of the ET model makes
it a compelling choice for accurate and robust epileptic seizure prediction
using EEG data.