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297 patient records with 37 clinical and cardiac computed tomography imaging features were used to develop machine learning models for predicting recurrence after catheter ablation for atrial fibrillation. The dataset, created by Xiaonan Han and published on figshare under CC-BY-4.0, includes distinct models for paroxysmal and persistent AF types. The models achieved AUC scores of 0.831 and 0.917, respectively, with explainable AI techniques applied.
Primary data file is a DOCX document; the actual tabular data may be embedded within it.