Atrial Fibrillation Recurrence Prediction Data with Cardiac CT Features

297 patient records from a retrospective study on atrial fibrillation recurrence after catheter ablation. The dataset includes 37 clinical and cardiac computed tomography imaging features, used to train machine learning models achieving AUC scores of 0.831 and 0.917. The research was authored by Xiaonan Han and last updated in April 2026.

Use Cases

• Predict recurrence of paroxysmal atrial fibrillation based on right atrial appendage short diameter.
• Predict recurrence of persistent atrial fibrillation based on right atrial volume.
• Develop clinical decision support tools for personalized post-ablation management.
• Apply explainable AI techniques to interpret feature importance in medical predictions.
• Compare performance of eight machine learning algorithms on a clinical dataset.

Strengths

• Contains 37 clinical and cardiac CT imaging features per patient.
• Models trained on the data achieved high predictive performance (AUC up to 0.917).
• Dataset is licensed under CC-BY-4.0 for open reuse.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Dataset size is 46.3 KB, indicating a very limited scope.

Provenance

Source

figshare

Collection Method

Retrospective collection from patients who underwent radiofrequency catheter ablation.

Freshness

Last updated 2026-04-29 05:57:17; freshness should be verified.