慢性阻塞性肺疾病合并血管疾病预测模型数据集

数据集获取（Figshare）

• 源站标题：A machine-learning model to identify concurrent vascular disease in symptomatic patients with chronic obstructive pulmonary disease
• DOI：10.6084/m9.figshare.30675962.v1
• 发布日期：2025-11-21
• 文件数：2 个
• 文件总体积：26.63 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/A_machine-learning_model_to_identify_concurrent_vascular_disease_in_symptomatic_patients_with_chronic_obstructive_pulmonary_disease/30675962

命令行下载

curl -L -o "iann_a_2588285_sm5595.docx" "https://ndownloader.figshare.com/files/59751053"
curl -L -o "iann_a_2588285_sm5594.docx" "https://ndownloader.figshare.com/files/59751056"

文件清单

• iann_a_2588285_sm5595.docx（14.18 KB）
• iann_a_2588285_sm5594.docx（12.45 KB）

文件总体积 26.63 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

Chronic obstructive pulmonary disease (COPD) is a complex, heterogeneous syndrome often accompanied by vascular diseases that worsen prognosis and quality of life. This study aimed to develop a machine learning model to identify concurrent vascular diseases in symptomatic COPD patients.
We retrospectively analyzed data from 6,274 COPD patients treated between July 2010 and July 2018. Patients were randomly split into training and validation sets (7:3). After feature selection using LASSO regression, eight machine learning algorithms—including Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, Neural Network, Convolutional Neural Network, AdaBoost, and Stacked Generalization (Stacking)—were applied to develop and validate predictive models. Performance was evaluated using AUC, calibration curves, and decision curve analysis (DCA).
The Stacking model achieved the highest AUC (0.867; 95% CI: 0.852–0.882), with 79.4% accuracy, 74.9% sensitivity, and 84.0% specificity. It also demonstrated excellent calibration and, on DCA, provided the highest net clinical benefit within the threshold probability range of 0.1–0.5. At a 0.2 threshold, the model could prevent approximately 35% of unnecessary interventions compared to a “treat-all” approach, while identifying about 75% of high-risk patients relative to a “treat-none” strategy.
The Stacking machine-learning model showed superior performance in identifying concurrent vascular disease among symptomatic COPD patients, offering strong discriminative ability, calibration, and clinical utility. It may serve as an effective decision-support tool to optimize diagnostic evaluation in this high-risk subgroup.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.30675962.v1（source: https://tandf.figshare.com/articles/dataset/A_machine-learning_model_to_identify_concurrent_vascular_disease_in_symptomatic_patients_with_chronic_obstructive_pulmonary_disease/30675962）。