Calis, Ahmet GokayErgezer, Halit2025-10-062025-10-062025979833156656297983315665552165-0608https://doi.org/10.1109/SIU66497.2025.11112012Isik UniversityIn this study, the effects of different SMOTE methods on machine learning algorithms for the imbalanced epilepsy dataset were investigated. After filtering, the imbalanced dataset was balanced with 5 different SMOTE methods and classified with various machine learning algorithms. Coarse-K-Nearest Neighbor, Bagged Trees, and Artificial Neural Networks models were evaluated in epilepsy detection. The performance of these different models was compared with Matthews Correlation Coefficient (MCC) and F1 Score metrics. The results showed that the Borderline-SMOTE algorithm had the highest F1 Score and MCC values among all machine learning algorithms. © 2025 Elsevier B.V., All rights reserved.In this study, the effects of different SMOTE methods on machine learning algorithms for the imbalanced epilepsy dataset were investigated. After filtering, the imbalanced dataset was balanced with 5 different SMOTE methods and classified with various machine learning algorithms. Coarse-K-Nearest Neighbor, Bagged Trees, and Artificial Neural Networks models were evaluated in epilepsy detection. The performance of these different models was compared with Matthews Correlation Coefficient (MCC) and F1 Score metrics. The results showed that the Borderline-SMOTE algorithm had the highest F1 Score and MCC values among all machine learning algorithms.trinfo:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/closedAccessEpilepsyMachine LearningSMOTEBagged TreesCoarse-KNNArtificial Neural NetworksConference Object10.1109/SIU66497.2025.111120122-s2.0-105015460554