Data Sheet 1：基于机器学习的心房颤动患者射频导管消融术后复发预测模型

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Machine learning-based prediction model for recurrence after radiofrequency catheter ablation in patients with atrial fibrillation.docx
• DOI：10.3389/fcvm.2025.1642409.s001
• 发布日期：2025-08-08
• 文件数：1 个
• 文件总体积：376.12 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning-based_prediction_model_for_recurrence_after_radiofrequency_catheter_ablation_in_patients_with_atrial_fibrillation_docx/29858528

命令行下载

curl -L -o "Data Sheet 1_Machine learning-based prediction model for recurrence after radiofrequency catheter ablation in patients with atrial fibrillation.docx" "https://ndownloader.figshare.com/files/57021962"

文件清单

• Data Sheet 1_Machine learning-based prediction model for recurrence after radiofrequency catheter ablation in patients with atrial fibrillation.docx（376.12 KB）

文件总体积 376.12 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

BackgroundThis study seeks to develop and validate a machine learning (ML) model for predicting atrial fibrillation (AF) recurrence at 12 months following radiofrequency catheter ablation (RFCA).
MethodsA total of 430 consecutive patients with atrial fibrillation undergoing first-time radiofrequency catheter ablation were retrospectively enrolled between June 2022 and December 2023. Patients were randomly assigned to either a training cohort (70%) or a testing cohort (30%). Four ML algorithms were employed to develop prediction models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and accuracy. The SHapley Additive exPlanations (SHAP) methodology was employed to interpret the best-performing model and quantify each feature’'s contribution to its predictions.
ResultsAmong the four machine learning algorithms evaluated, the Light Gradient Boosting Machine (LightGBM) model showed promising predictive performance on the testing set, achieving an accuracy of 0.721 and an AUC of 0.848 (95% CI: 0.778–0.919). Interpretation of the LightGBM model using SHAP analysis identified B-type natriuretic peptide (BNP) and the neutrophil-to-lymphocyte ratio (NLR) as the most impactful predictors for AF recurrence. The analysis revealed that higher levels of BNP and NLR were strongly associated with an increased risk of recurrence, whereas higher levels of albumin and lymphocyte count were protective. Other significant predictors included left atrial diameter (LAD) and nonparoxysmal atrial fibrillation (NPAF).
ConclusionMachine learning-based models show modest but promising performance for assessing AF recurrence risk after RFCA using routine clinical data. While requiring extensive external validation before clinical application, these models highlight the potential of ML to inform future risk stratification and guide personalized follow-up strategies.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2025.1642409.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning-based_prediction_model_for_recurrence_after_radiofrequency_catheter_ablation_in_patients_with_atrial_fibrillation_docx/29858528）。