Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning: Code repository

Background: Following atrial fibrillation ablation, it is challenging to distinguish patients who will remain arrhythmia-free from those at risk for recurrence. New explainable machine learning (xML) techniques allow for systematic assessment of arrhythmia recurrence risk following catheter ablation. We aim to develop an xML algorithm that predicts recurrence and reveals key risk factors to facilitate better follow-up strategy after an ablation procedure. Methods: We reconstructed pre-and post-ablation models of the left atrium (LA) from late gadolinium enhanced magnetic resonance (LGE-MRI) for 67 patients. Patient-specific features (LGE-based measurements of pre/post-ablation arrhythmogenic substrate, LA geometry metrics, computational simulation results, and clinical risk factors) trained a random forest classifier to predict recurrent arrhythmia. We calculated each risk factor’s marginal contribution to model decision making via SHapley Additive exPlanations (SHAP). Here we prov..., , # Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning: Code repository Dataset DOI: [10.5061/dryad.4tmpg4fp9](10.5061/dryad.4tmpg4fp9) ## Description of the data and file structure ### Explainable machine learning code Code used for random forest classifier model development, testing, and SHAP analysis-based explanations through the associated publication \"Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning\" published in *Communications Medicine*. #### Installation Written for Python version `3.10.11`. Use pip ([https://pypi.org/](https://pypi.org/)) to install dependencies from `requirements.txt`. #### Model development, explanations, and testing Train a model and create explanations by updating the `BASE_DIR` (lines 34-36) of `train_explain_cv.py` Test a model on an entirely new dataset by updating the `BASE_DIR` (lines 34-36) of `external_validation.py` These scripts a...,