心脏病UCI数据集
该数据集是一个以表格形式存储的临床医学数据集，包含患者的年龄、性别、血压、胆固醇等14个关键临床指标，目标字段为心脏病严重程度分级。它主要用于机器学习分类任务，如心脏病风险预测和诊断辅助研究，是医学数据分析与人工智能应用领域的经典基准数据集之一。
基本信息
资源简介
心脏病UCI数据集
【数据集背景】
本数据集源站标题为《sharmaroshan/Heart-UCI-Dataset》。源站首次发布：2019-04-20。
【数据内容】
源站原始描述：Analyzing the Features which leads to heart diseases and visualizing the models'' performance and important features using eli5, shap and pdp. # Heart-UCI-Dataset Analyzing the Features which leads to heart diseases and visualizing the models'' performance and important features using eli5, shap and pdp. classification classificationx 3004 machine learning > classification , binary classification binary classificationx 348 problem type > binary classification , health healthx 140 society and socia…
【主题与分类】
主题标签：data-analysis、data-visualization、feature-importance、machine-learning、model-evaluation。
【获取与许可】
源站页面：https://github.com/sharmaroshan/Heart-UCI-Dataset
使用许可：GPL-3.0。
【使用注意】
· 源站最后修改于 2019-05-13，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/RoshanLPU/Heart-UCI-Dataset.git
curl -L -o repo.zip https://github.com/RoshanLPU/Heart-UCI-Dataset/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
Heart-UCI-Dataset
Analyzing the Features which leads to heart diseases and visualizing the models’’ performance and important features using eli5, shap and pdp.
classification
classificationx 3004
machine learning > classification
, binary classification
binary classificationx 348
problem type > binary classification
, health
healthx 140
society and social sciences > society > health
, biology
biologyx 87
natural and physical sciences > biology
Description
Context
This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The “goal” field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4.
Content
Attribute Information:
- age
- sex
- chest pain type (4 values)
- resting blood pressure
- serum cholestoral in mg/dl
- fasting blood sugar > 120 mg/dl
- resting electrocardiographic results (values 0,1,2)
- maximum heart rate achieved
- exercise induced angina
- oldpeak = ST depression induced by exercise relative to rest
- the slope of the peak exercise ST segment
- number of major vessels (0-3) colored by flourosopy
- thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
The names and social security numbers of the patients were recently removed from the database, replaced with dummy values. One file has been “processed”, that one containing the Cleveland database. All four unprocessed files also exist in this directory.
To see Test Costs (donated by Peter Turney), please see the folder “Costs”
Acknowledgements
Creators:
- Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
- University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
- University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
- V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.
Donor: David W. Aha (aha ‘’@‘’ ics.uci.edu) (714) 856-8779
Inspiration
Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).
See if you can find any other trends in heart data to predict certain cardiovascular events or find any clear indications of heart health.
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))
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
