Paper Title :An Analysis of Epilepsy Detection and Classification Using Machine Learning Techniques
Author :Mantra Jain, Harshit Bhardwaj, Anshsrivastav
Article Citation :Mantra Jain ,Harshit Bhardwaj ,Anshsrivastav ,
(2024 ) " An Analysis of Epilepsy Detection and Classification Using Machine Learning Techniques " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 99-106,
Volume-12,Issue-3
Abstract : Epilepsy, a mental disorder characterized by seizures and uncertainty, remains a significant medical problem.
Timely and accurate detection of epilepsy is very important for diagnosis, treatment and patient management. Considering
that seizures can occur suddenly and without warning, it is important to have a system that can detect seizures. A
comprehensive review of the electroencephalogram (EEG) recording is required to accurately identify these seizures. In
recent years, the convergence of machine learning and medicine has shown promise in improving the diagnosis and
categorization of epilepsy. This summary provides a brief overview of the report on epilepsy diagnosis and classification
analysis, which includes various machine learning algorithms such as K-Nearest Neighbour (KNN), Logistic Regression,
Naive Bayes, Random Forest, Support Vector Machine (SVM) and Decision Trees. This study provides a brief summary of a
report on epilepsy detection and classification through machine learning. This report explores the evolving field of epilepsy
diagnosis and reviews the various machine learning algorithms, datasets, and computational techniques currently in use. The
overall aim of this paper is to highlight the potential of machine learning to improve our understanding and management of
epilepsy.
Keywords - Electroencephalogram, Epilepsy, Seizures
Type : Research paper
Published : Volume-12,Issue-3
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-20650
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Copyright: © Institute of Research and Journals
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Published on 2024-06-26 |
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