Epileptic seizures classification in EEG using PCA based genetic algorithm through machine learning

In this research, a Principal Component Analysis (PCA) with Genetic Algorithm based Machine Learning (ML) approach is developed for the binary classification of epileptic seizures from the EEG dataset. The proposed approach utilizes PCA to reduce the number of features for binary classification of epileptic seizures and is applied to the existing machine learning models to evaluate the model performance in comparison to the higher number of features. Here, Genetic Algorithm (GA) is employed to tune the hyperparameters of the machine learning models for identifying the best ML model. The proposed approach is applied to the UCI epileptic seizure recognition dataset, which is originated from the EEG dataset of Bonn University. As a preliminary analysis of the proposed approach, the data analysis result shows a significant reduction in the number of features but has minimal impact on the ML performance parameters in comparison to the existing ML method.