Hybrid CNN-Transformer-WOA model with XGBoost-SHAP feature selection for arrhythmia risk prediction in acute myocardial infarction patients.

ABSTRACT:

Background

Arrhythmia is a frequent and serious complication of acute myocardial infarction (AMI), leading to higher mortality. Early prediction is critical for timely intervention, but existing methods are limited by poor accuracy and low clinical applicability.

Methods

We developed a novel hybrid model integrating convolutional neural network (CNN), Transformer, and Whale Optimization Algorithm (WOA) for arrhythmia prediction in AMI patients. A two-stage feature selection using XGBoost and SHAP identified the top 10 clinical predictors from 45 variables. The model was trained and validated using stratified 10-fold cross-validation on a retrospective cohort of 2,084 patients. Performance was compared with traditional machine learning and deep learning baselines using accura

SUBMITTER: Li L 

PROVIDER: S-EPMC12330184 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature