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
该数据集是一个用于心脏病预测的医学数据集，包含76个原始属性，但通常仅使用其中14个关键属性进行分析。数据主要来源于克利夫兰数据库，这是机器学习领域研究心脏病预测时最常用的数据库。数据以表格形式呈现，包含患者的年龄、性别、血压、胆固醇等临床指标，目标字段为患者心脏病的严重程度，数值范围从0（无心脏病）到4。该数据集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。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
暂未开放公开下载
Tips: 该数据集属于公开下载，应该可以免费公开下载。
免登录有偿下载
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提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
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))
精度瓶颈？数据缺失？
当前公开数据无法满足您的算法精度？千方提供针对 心脏病 的高质量、多模态真实临床数据定制解决方案。
