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This document discusses a study on predicting epileptic seizures using machine learning models, specifically focusing on the preictal state detected through EEG data. The study employs wavelet analysis to filter EEG signals and utilizes classifiers such as K-Nearest Neighbor, Support Vector Machine, and Random Forest, achieving accuracies of 88%, 84%, and 94% respectively, with Random Forest performing the best. The research highlights the importance of effective signal processing and classification techniq

Author

faheem.edu999

Language

EN