Advancing Epilepsy Disease Classification through Machine Learning and Deep Learning Models Utilizing EEG Data.

Epilepsy disease is a neurological condition marked by recurring seizures that has a big effect on people’s life. Effective management and therapy depend on a prompt and correct diagnosis. The traditional methods, including medical history checks and MRI-based diagnoses, are costly, time-consuming, and often create incorrect results. In order to overcome the drawbacks of conventional and expensive diagnostic techniques, this work offers a machine-learning and deep learning-based method for the classification of epilepsy disease utilizing EEG signal data. The proposed study uses the EEG signal dataset collected from the UCI machine learning repository. This study shows the potential for automating the classification of epilepsy disease by using EEG signal data, opening up a potentially new path for the early detection of disease. Proposed CNN model achieved the highest accuracy of 98.4% among all classifiers, which is quite good.