Paper Title
An Analysis of Epilepsy Detection and Classification Using Machine Learning Techniques

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