冠状动脉疾病诊断特征数据集

数据集获取（Figshare）

• 源站标题：Features.csv
• DOI：10.6084/m9.figshare.8091332.v1
• 发布日期：2019-05-08
• 文件数：1 个
• 文件总体积：15.82 KB
• 源站页面：https://figshare.com/articles/dataset/Features_csv/8091332

命令行下载

curl -L -o "Features.csv" "https://ndownloader.figshare.com/files/15090965"

文件清单

• Features.csv（15.82 KB）

文件总体积 15.82 KB（源站 API 实测）。

数据加载示例（表格/文本类）

import pandas as pd, glob, os

files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))

数据说明

This Database belongs to “A Database for Using
Machine Learning and Data Mining Techniques for Coronary Artery Disease
diagnosis” research paper. This database includes 15 tables. We chose to index
papers related to CAD detection using machine learning and data mining
approaches that are published between 1992 and 2018. These criteria result in
126 papers in which 490 authors contributed to these papers. These papers
studied 68 different datasets with almost more than 360 distinct features
collected in 18 countries from Asia, Europe, and America. In these papers, 140
different machine learning or data mining techniques were applied to diagnose
CAD. To use csv file, open it in excel or import it in your database.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.8091332.v1（source: https://figshare.com/articles/dataset/Features_csv/8091332）。