数据表1：基于机器学习的心血管疾病风险预测

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Web-based cardiovascular disease risk prediction using machine learning.docx
• DOI：10.3389/frai.2026.1690664.s001
• 发布日期：2026-02-13
• 文件数：1 个
• 文件总体积：433.51 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Web-based_cardiovascular_disease_risk_prediction_using_machine_learning_docx/31331197

命令行下载

curl -L -o "Data Sheet 1_Web-based cardiovascular disease risk prediction using machine learning.docx" "https://ndownloader.figshare.com/files/61861558"

文件清单

• Data Sheet 1_Web-based cardiovascular disease risk prediction using machine learning.docx（433.51 KB）

文件总体积 433.51 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

Cardiovascular disease (CVD) remains the foremost contributor to global illness and death, underscoring the critical need for effective tools that can predict risk at early stages to support preventive care and timely clinical decisions. With the growing complexity of healthcare data, machine learning has shown considerable promise in extracting insights that enhance medical decision-making. Nonetheless, the effectiveness and clarity of machine learning models largely rely on the relevance and quality of input features. In this work, we explored and compared four feature-selection strategies—Pearson correlation + Chi-squared test, Alternating Decision Tree (ADT)-based scoring, Cross-Validated Feature Evaluation (CVFE), and Hypergraph-Based Feature Evaluation (HFE)—to identify the most predictive factors for CVD risk. Our analysis utilized data from the National Health and Nutrition Examination Survey (NHANES), administered by the National Center for Health Statistics under the Centers for Disease Control and Prevention (CDC), encompassing demographic, clinical, laboratory, and survey data collected across the U.S. from August 2021 through August 2023. Distinct sets of features obtained through these selection techniques were used to develop random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) models, which were then assessed for predictive effectiveness. To improve clarity and understanding of model decision-making, SHapley Additive exPlanations (SHAP) was used to interpret feature contributions in the top-performing model. Among the evaluated methods, the HFE approach combined with SVM achieved the highest overall accuracy (82.84%) and AUC (0.9027), outperforming both classical and alternative strategies. The most influential predictors included age, total cholesterol, history of high blood pressure, use of cholesterol-lowering medication, recent prescription medication use, lifetime smoking history, family income-to-poverty ratio, gender, educational attainment, and red cell distribution width. The web application, accessible at https://shiny.tricities.wsu.edu/cvdr-prediction/, presents predictive results, probability score

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/frai.2026.1690664.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Web-based_cardiovascular_disease_risk_prediction_using_machine_learning_docx/31331197）。