预测中风风险数据集

数据集获取（Figshare）

• 源站标题：<b>Predicting Stroke Risk Dataset</b>
• DOI：10.6084/m9.figshare.28668398.v4
• 发布日期：2025-04-10
• 文件数：1 个
• 文件总体积：322.08 KB
• 源站页面：https://figshare.com/articles/dataset/_b_Predicting_Stroke_Risk_Dataset_b_/28668398

命令行下载

curl -L -o "StrockDataset.csv" "https://ndownloader.figshare.com/files/53444630"

文件清单

• StrockDataset.csv（322.08 KB）

文件总体积 322.08 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

This study addresses stroke as a critical global health issue by employing a comprehensive, data-driven approach to improve early risk prediction and intervention. Utilizing a dataset of 5,110 records, the research combines statistical analysis, machine learning (ML) classification, clustering techniques, and survival modeling to identify key predictors of stroke. Descriptive analysis highlights age, average glucose level, BMI, hypertension, and heart disease as the most significant risk factors, with stroke prevalence reaching 13.25% among hypertensive individuals and 17.03% among those with heart disease. Former and current smokers also demonstrate elevated stroke risk. Clustering using PCA and t-SNE reveals high-risk groups characterized by older age and high glucose levels. ML evaluation shows that XGBoost provides the best precision-recall balance, while Naïve Bayes achieves the highest recall (0.404), offering greater sensitivity to stroke detection. Feature importance analysis consistently ranks glucose, BMI, and age as dominant predictors, with XGBoost assigning high weight to cardiovascular conditions. Survival analysis using Kaplan-Meier and Cox regression models shows stroke risk increases sharply after age 60, with hypertension linked to a 31.9% higher risk. The results emphasize the value of early screening and targeted intervention, suggesting future improvements via class-balancing techniques and real-time clinical tools.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.28668398.v4（source: https://figshare.com/articles/dataset/_b_Predicting_Stroke_Risk_Dataset_b_/28668398）。