用于复现C5.0模型的匿名数据集

数据集获取（Figshare）

• 源站标题：Anonymized dataset used to reproduce C5.0 models.
• DOI：10.1371/journal.pone.0334829.s010
• 发布日期：2025-10-17
• 文件数：1 个
• 文件总体积：79.28 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Anonymized_dataset_used_to_reproduce_C5_0_models_/30388053

命令行下载

curl -L -o "pone.0334829.s010.csv" "https://ndownloader.figshare.com/files/58845843"

文件清单

• pone.0334829.s010.csv（79.28 KB）

文件总体积 79.28 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

Echocardiographic left ventricular hypertrophy (Echo-LVH) is frequently underdetected by traditional electrocardiogram (ECG) criteria due to limited sensitivity. We investigated whether integrating ECG with vectorcardiography (VCG) using a clinically interpretable machine learning algorithm (C5.0) could improve diagnostic performance. We analyzed ECG and VCG data from 664 patients, 42.8% of whom had Echo-LVH. The study introduced three new criteria—Marcos VCG, Marcos VCG-ECG, and Marcos VCG-ECGsp—named in honor of the software used for VCG synthesis, and compared their diagnostic performance against 23 established ECG criteria, including Cornell voltage, Peguero-Lo Presti, and Sokolow-Lyon. Marcos VCG-ECGsp, optimized for higher specificity, was included to evaluate trade-offs in performance. Validation was performed using train/test split and 10-fold cross-validation. Marcos VCG-ECG achieved higher AUC than Cornell voltage in both training (0.81 vs. 0.68, p < 0.0001) and testing (0.78 vs. 0.69, p = 0.04). The new criteria also showed superior sensitivity compared to Peguero-Lo Presti, the most sensitive traditional criterion (73.1%, 62.4%, 55.9% vs. 30.1%, p < 0.0001). While specificity was lower than Cornell (81.1% vs. 96.4%, p = 0.017), it remained acceptable, reflecting a clinically relevant trade-off favoring detection over false positives. In conclusion, integrating ECG with VCG through machine learning enances Echo-LVH detection, delivering superior sensitivity while preserving specificity. The proposed criteria are clinically interpretable, highlight the novelty of combining two electrical spectra, and hold potential to impact routine diagnostic practice.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0334829.s010（source: https://plos.figshare.com/articles/dataset/Anonymized_dataset_used_to_reproduce_C5_0_models_/30388053）。