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 provide
code for xML model training, validation, and explanation in our associated
publication "Predicting arrhythmia recurrence post-ablation in atrial
fibrillation using explainable machine learning" in Communications
Medicine. This code serves to train and test a random forest classifier
and then applies SHAP analysis offers explanations of model
classifications. Results: The classifier accurately predicts post-ablation
arrhythmia recurrence (mean receiver operating characteristic [ROC] area
under the curve [AUC]: 0.80±0.04; mean precision-recall [PR] AUC:
0.82±0.08). SHAP analysis reveals that of 89 features tested, the key
population risk factors for recurrence are: large left atrium, low
LGE-quantified post-ablation scar in the atrial floor region, and previous
attempts at direct current cardioversion. We also examine patient-specific
recurrence predictions, since xML allows us to understand why a particular
individual can have large prediction weights for some categories without
tipping the balance towards an incorrect prediction. Finally, we validate
our model in a completely new, 15-patient retrospective holdout cohort
(80% correct). Conclusion: Our SHAP-based explainable machine learning
approach is a proof-of-concept clinical tool to explain arrhythmia
recurrence risk in patients who underwent ablation by combining
patient-specific clinical profiles and LGE-derived data.