Hyperparameter tuning improves atrial fibrillation detection accuracy
Enhancing Atrial Fibrillation Detection Through Hyperparameter Optimization
Machine Learning
Summary
Atrial fibrillation is a heart condition that can be detected using computer models analyzing heart rate data. The authors studied how adjusting settings called hyperparameters can improve the accuracy of different computer models. They tested several machine learning and deep learning methods and found that one method, using Hyperband to tune an XGBoost model, worked best. This helps build better tools for detecting atrial fibrillation in real-world medical data.
What this means in practice
- •For hospital data teams: Improve automated atrial fibrillation detection pipelines by integrating Hyperband-tuned XGBoost models for better diagnosis accuracy on clinical ECG data.
- •For mobile health app developers: Develop smartphone apps that incorporate optimized machine learning models to detect atrial fibrillation from ECG signals more reliably in diverse patient populations.$Commercial implications: Enables creation of consumer health monitoring apps that provide more accurate AF detection, increasing market appeal and health impact.
Authors
Sara Gjorgjieva, Ilinka Ivanoska, Marjan Gusev
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.