An Investigation on Epileptic Seizure Classification Using Machine Learning and Multiple Feature Selection Strategies

Epilepsy is a serious brain illness characterized by abnormal brain activity. To build a better computer-aided diagnosis (CAD) solution, we provide a comprehensive comparison of four prominent Machine Learning (ML) algorithms for epilepsy classification: Naive Bayes (NB), K-Nearest Neighbor (K-NN), Decision Tree (DT), and Logistic Regression (LR). One of the goals of the proposed model was to address current challenges such as outliers, low performance, and to build a stable and accurate classification model. As a result, few notable sampling techniques are employed to address the problems associated with the dataset. Cohen's distribution class's Time-frequency domain (TFD) technique is used to extract significant properties from the EEG dataset. Following the retrieval of the sample data, different algorithms were used to retain the most suitable features from the derived data, such as Univariate Logistic Regression based feature selection (ULOGR), Gini-Score based significant feature selection (GSFR), and Information Gain based features (IGFR). Following the classification, the performance of classifiers was compared before and after the addition of feature selection methods to the two datasets using the different t-f methods utilized in this study. Our findings suggest that the performance of the classifier improves i) after dimensionality reduction ii) if the t-f technique employs frequency and time smoothing windows. For the UCI Dataset, the obtained findings reveal that the NB classifier excels the other classifiers with a 96% accuracy rate.