The Use of Machine Learning in Predicting Neurological Disorders for Epilepsy

Saniya Vaish

doi:10.36948/ijfmr.2024.v06i03.22870

The Use of Machine Learning in Predicting Neurological Disorders for Epilepsy

Author(s)	Saniya Vaish
Country	United States
Abstract	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.
Keywords	Epilepsy, Machine Learning, EEG, Neural Networks, Predictive Modeling, Seizure Prediction, Support Vector Machines, Random Forests, Long Short-Term Memory, Convolutional Neural Networks, Hybrid Models
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-06-17
Cite This	The Use of Machine Learning in Predicting Neurological Disorders for Epilepsy - Saniya Vaish - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.22870
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.22870
Short DOI	https://doi.org/gt2bzq

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The Use of Machine Learning in Predicting Neurological Disorders for Epilepsy

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