StackTrans–Multimodal Heart Disease Detection Using Stacked Transformer Fusion Framework

Cardiac disease diagnosis demands precise interpretation of complex physiological signals, yet existing systems often rely on unimodal data and lack adaptive fusion strategies. Most conventional frameworks fall short in capturing intermodal dependencies and adjusting to performance variations across heterogeneous inputs. This study introduces StackTrans, a transformer-based multimodal classification framework designed to improve diagnostic accuracy through ECG and PCG signal integration. The architecture comprises modality-specific transformer encoders, a bidirectional cross-modal fusion transformer that facilitates latent-level attention between modalities, and a stacked ensemble mechanism governed by a meta-learner. Residual learning modules enhance prediction refinement, while entropy-guided adaptive voting improves confidence-weighted decision reliability. The PCG and ECG modules are independently trained using the PhysioNet 2016 and MIT-BIH Arrhythmia datasets, respectively, and integrated through joint inference. Evaluations using TensorFlow on an NVIDIA RTX GPU demonstrate that StackTrans attains a precision of 98.6%, an F1 score of 98.4%, and an AUC of 0.99—outperforming unimodal ECG and PCG models by 2.5% and 7.5%, respectively.