The Use of Machine Learning in Predicting Neurological Disorders for Epilepsy

Epilepsy, a chronic neurological disorder characterized by recurrent seizures, affects millions of individuals worldwide. Early diagnosis and accurate prediction of epileptic seizures are crucial for effective treatment and management. With recent advancements in machine learning (ML) algorithms and the availability of large-scale EEG datasets, there is growing interest in utilizing ML techniques for automated seizure prediction and diagnosis. This research paper explores the application of various ML models, including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and hybrid models, in predicting epileptic seizures using EEG data. Leveraging the Temple University Hospital EEG Corpus and other publicly available datasets, this study aims to evaluate the performance of these models and provide insights into their practical applicability in clinical settings. The findings highlight the potential of ML-based approaches to improve early detection and management of epilepsy, offering promising avenues for enhancing patient care and outcomes.