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arXiv · 2610.40001

Enhancing Atrial Fibrillation Detection Through Hyperparameter Optimization

Sara Gjorgjieva·Ilinka Ivanoska·Marjan Gusev
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Abstract

This study explores the role of hyperparameter optimization (HPO) in enhancing the detection of Atrial Fibrillation (AF) using both machine learning and deep learning models. We evaluated a range of standard classifiers - Decision Tree, Random Forest, and XGBoost classifiers, alongside deep learning architectures including Recurrent Neural Networks, Deep Neural Networks, and Siamese Neural Networks. To fine-tune these models, we applied four HPO methods: Genetic Algorithm, Hyperband, Optuna, and SMAC3. The experiments were conducted using inter-patient and intra- patient data splits to assess model generalization and robustness in the presence of class imbalance, which is reflective of real-world AF occurrence. Our results, based on PhysioNet electrocardiogram datasets, showed that the Hyperband-optimized XGBoost model outperformed all others, achieving a peak F1 score of 96.95%. These findings highlight the critical role of suitable HPO strategies in enhancing the accuracy and robustness of AF classification outcomes in clinical settings.

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Sara Gjorgjieva, Ilinka Ivanoska, Marjan Gusev. 2026-10-29. Enhancing Atrial Fibrillation Detection Through Hyperparameter Optimization. https://arxiv.org/abs/2610.40001

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