Decision support system based on ensemble models in distinguishing epilepsy types

This study aimed to classify patients' focal (frontal, temporal, parietal, occipital), multifocal, and generalized epileptiform activities based on EEG findings using artificial intelligence models. The study included 575 patients followed in the Neurology Epilepsy Polyclinics of Adana City Training and Research Hospital between June 2021 and July 2024. Patient history, examination findings, seizure characteristics and EEG results were retrospectively reviewed to create a comprehensive database. Initially, machine learning architectures were applied to differentiate generalized and focal epilepsy. Subsequently, EEG findings were categorized into eight subgroups, and machine learning methods were utilized for classification. Three AI models-Multilayer Perceptron (MLP), Random Forest, and Support Vector Machine (SVM)-were employed. The dataset was further improved through data augmentation with SMOTE. The initial deep learning model achieved 89 % accuracy, recall, and F1 scores. Then, Optuna framework was incorporated into model to optimize hyperparameters, thus the accuracy reached 96 %. In comparison, the proposed ensemble model combining MLP, SVM and XGBoost achieved the highest accuracy of 98 %. The study demonstrates that data augmentation and ensemble AI models can provide robust decision support for physicians in classifying epilepsy types.