An interpretable machine learning framework using XGBoost with Focal Loss achieved an AUC of 0.837 for detecting left ventricular hypertrophy from magnetocardiography signals in an independent validation set.
Observational (n=481)
No
Does a machine learning-enhanced magnetocardiography (MCG) framework improve the diagnostic accuracy for detecting left ventricular hypertrophy?
An interpretable machine learning framework using magnetocardiography (MCG) provides high diagnostic accuracy for automated left ventricular hypertrophy detection, identifying T-wave magnetic polarity as a key predictor.
Absolute Event Rate: 0.837% vs 0.735%
Magnetocardiography (MCG) provides high-resolution spatiotemporal insights into cardiac electrophysiology but remains underutilized for left ventricular hypertrophy (LVH) diagnosis due to a lack of interpretable analytical tools. We propose a novel interpretable machine learning framework that systematically decodes MCG signals across four complementary domains: temporal waves, spatial waves, current source imaging, and dynamic characterization. To address class imbalance, we integrated Focal Loss into the XGBoost objective function. In a dataset of 481 subjects, our model achieved an AUC of 0.902 in cross-validation and 0.837 in independent validation, significantly outperforming conventional baselines. Notably, SHapley Additive exPlanations (SHAP) identified T-wave magnetic polarity as the most influential predictor, offering new perspectives on the electrophysiological remodeling of hypertrophied myocardium. This framework bridges the gap between raw sensor data and clinical decision-making, providing a robust tool for automated LVH detection.
Chen et al. (2026) conducted an observational in Left ventricular hypertrophy (LVH) (n=481). XGBoost-Focal Loss machine learning framework using magnetocardiography (MCG) signals vs. Conventional machine learning baselines (e.g., Logistic Regression) was evaluated on Area Under the Curve (AUC) for LVH detection on independent validation set. An interpretable machine learning framework using XGBoost with Focal Loss achieved an AUC of 0.837 for detecting left ventricular hypertrophy from magnetocardiography signals in an independent validation set.