Heart Disease Dataset

Heart Disease Diagnosis
Dataset

The classic Cleveland heart disease dataset from the UCI Machine Learning Repository, containing 297 patient samples and 13 clinical examination indicators, widely used for binary classification prediction and medical data analysis research.

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297 samples 14 features CC BY 4.0 license Cleveland Clinic (1988)
Heart Disease Dataset
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297
Total Samples
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14
Feature Dimensions
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2
Diagnostic Classes
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CC BY 4.0
Open License Agreement

Dataset Highlights

Classic medical dataset, suitable for clinical data analysis from beginner to advanced levels

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Real Clinical Data

The data comes from real patient records at the Cleveland Clinic, including 13 clinical indicators such as age, gender, type of chest pain, blood pressure, cholesterol, etc.

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Classic Binary Classification Task

Predict whether a patient has heart disease, making it an ideal dataset for learning classification algorithms like logistic regression, SVM, and random forests.

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Introduction to Medical AI

One of the most commonly used medical datasets, helping to understand how to apply machine learning to clinical decision support systems.

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Diversity of Features

Includes continuous (blood pressure, cholesterol) and categorical (type of chest pain, ECG results) features, suitable for data preprocessing practice.

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Academic Literature Support

The dataset has been collected by multiple cardiology experts and is widely cited in academic journals such as the International Journal of Machine Learning.

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UCI Authoritative Source

Originating from the UCI Machine Learning Repository, it has been the standard benchmark dataset in the field of heart disease prediction for nearly 40 years.

Applicable Scenarios

From classroom teaching to medical research, it can provide value

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Clinical Decision Support

Build heart disease risk prediction models to assist doctors in preliminary screening and diagnostic reference

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Binary Classification Modeling

Use algorithms such as logistic regression, SVM, and random forests for heart disease prediction

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Feature Importance

Analyze which clinical indicators are most predictive of heart disease diagnosis

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Data Visualization

Explore the distribution and correlation of various indicators, suitable for medical data EDA practice

Medical AI Binary Classification Clinical Data Beginner Dataset Benchmarking

Data Preview

The following are examples of the first few rows of the Cleveland heart disease dataset

CSV
age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target
63.0,1.0,1.0,145.0,233.0,1.0,2.0,150.0,0.0,2.3,3.0,0.0,6.0,0
67.0,1.0,4.0,160.0,286.0,0.0,2.0,108.0,1.0,1.5,2.0,3.0,3.0,2
67.0,1.0,4.0,120.0,229.0,0.0,2.0,129.0,1.0,2.6,2.0,2.0,7.0,1
37.0,1.0,3.0,130.0,250.0,0.0,0.0,187.0,0.0,3.5,3.0,0.0,3.0,0
41.0,0.0,2.0,130.0,204.0,0.0,2.0,172.0,0.0,1.4,1.0,0.0,3.0,0

3 Steps to Get Started Quickly

From browsing to analysis, you can start your data science project in minutes

01

Browse the Dataset

View dataset details on the Ace Data Cloud platform, understand field descriptions, sample size, and licensing agreements, and other metadata.

02

Download Data

Download the CSV file (18 KB), data is ready to use, no additional cleaning required.

03

Load and Analyze

Use pandas.read_csv() to load the data, and start exploratory analysis, modeling, and visualization.

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Start Exploring Heart Disease Diagnosis Data

A classic medical dataset, open license, available for immediate download. Whether you are a beginner in medical AI or an experienced data scientist, this dataset is worth trying.

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