Benchmarking Lightweight Machine Learning Models for Epileptic Seizure Recognition: Accuracy, Calibration, and Robustness Analysis

Epileptic seizure recognition is a critical task in clinical decision support systems, where both accuracy and reliability of predictions directly affect patient outcomes. While deep learning architectures such as CNNs and LSTMs are widely applied to EEG-based seizure detection, many publicly available seizure datasets consist of precomputed EEG-derived features, making the problem fundamentally tabular rather than raw-signal based. In such settings, the necessity and added value of complex deep learning pipelines remain unclear, and prior studies have largely emphasized classification accuracy while giving more limited attention to calibration, robustness, and deployment efficiency. In this work, we present a systematic benchmark of lightweight machine learning models—Logistic Regression, Random Forest, XGBoost, LightGBM, and CatBoost—on the Epileptic Seizure Recognition dataset. We evaluate performance across multiple dimensions: discriminative ability (accuracy, macro-F1, ROC-AUC, PR-AUC), confidence calibration (Brier score, calibration and reliability diagrams), and robustness under Gaussian feature perturbations. Our results show that LightGBM achieves 98.04% accuracy, a ROC-AUC of 0.9971, and a Brier score of 0.0166, while maintaining stable performance under the tested noise levels. Notably, all gradient boosting methods substantially outperform Logistic Regression, indicating that nonlinear feature interactions are critical for this task. Compared with prior deep learning approaches on the same dataset, these lightweight models achieve competitive performance at a fraction of the computational cost. These findings show that tabular machine learning methods deserve serious consideration for EEG-derived feature classification tasks, particularly in resource-constrained clinical settings where efficiency, calibration, and robustness are as important as raw accuracy.