AUTOMATIC EPILEPSY DETECTING USING MACHINE LEARNING BY NAIVE BAYES TECHINIQUE

Abstract

Epilepsy is one of the most common neurological disorders characterized by recurrent and unpredictable seizures caused by abnormal electrical activity in the brain. Early and accurate detection of epileptic seizures is crucial for timely medical intervention and effective treatment planning. Traditional epilepsy diagnosis primarily relies on manual interpretation of Electroencephalogram (EEG) signals by neurologists, which is time-consuming, labor-intensive, and susceptible to human error. Therefore, there is a growing need for automated and intelligent systems that can assist healthcare professionals in detecting epilepsy with high accuracy and efficiency. This project proposes an Automatic Epilepsy Detection System using Machine Learning based on the Naive Bayes Technique. The system utilizes EEG signal datasets obtained from patients and extracts relevant features that represent the characteristics of normal and epileptic brain activities. The collected data undergo preprocessing steps such as noise removal, normalization, and feature selection to improve the quality of the input data. The Naive Bayes classifier is then trained using these extracted features to classify EEG signals into epileptic and nonepileptic categories. Despite its assumption of feature independence, the Naive Bayes algorithm is computationally efficient, easy to implement, and capable of delivering reliable classification performance for medical diagnostic applications.