A Comprehensive Analysis of ML and DL Approaches for Epileptic Seizure Prediction

Epilepsy is a health condition that affects many individuals, potentially causing significant harm to the brain. Seizures, a common symptom of epilepsy, can result in severe injuries. Early detection of seizures is crucial to minimizing the impact of these injuries. A seizure prediction system aims to recognize when a seizure is likely to occur, facilitating timely intervention for individuals with epilepsy. In order to predict epileptic seizures, we used machine learning (ML) and deep learning (DL) models in this work. The machine learning methods used are SVM, Random Forest, Decision Tree, KNN, and Logistic Regression. Conventional neural networks, artificial neural networks, convolutional neural networks, recurrent neural networks, and long short-term memory are the DL algorithms used. Of these models, the Long Short-Term Memory deep learning model outperforms in terms of accuracy.