UCI Heart Disease Data: Clinical Indicators for ML
Catalog Summary
"The world-famous Cleveland dataset for heart disease classification. Contains 303 instances with 14 key clinical attributes like chest pain type and resting blood pressure."
Catalog Notes
This is the most cited dataset in the history of Medical Machine Learning. Sourced from the University of California, Irvine (UCI) repository, it is used to benchmark classification algorithms.
Dataset Attributes:
- age: Age in years.
- sex: (1 = male; 0 = female).
- cp: Chest pain type (4 values).
- trestbps: Resting blood pressure (in mm Hg).
- chol: Serum cholestoral in mg/dl.
- fbs: Fasting blood sugar > 120 mg/dl.
- restecg: Resting electrocardiographic results.
- thalach: Maximum heart rate achieved.
- target: Diagnosis of heart disease (angiographic disease status).
Why use this?
Unlike raw raw sensor logs, this dataset is cleaned and labeled, making it perfect for testing Logistic Regression, Random Forest, and Neural Networks without heavy preprocessing.
Source: UCI Machine Learning Repository - Attribution CC BY 4.0.
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on External.
Preview on ExternalCite This Catalog Record
IoTDataset.com (2026). UCI Heart Disease Data: Clinical Indicators for ML — catalog record. [Dataset]. IoTDataset.com. https://iotdataset.com/data/uci-heart-disease-classification
The publisher's preferred dataset citation is not recorded. The text above cites this IoTDataset.com catalog record, not the underlying dataset.
Catalog publisher: IoTDataset.com (2026)
Indexed by IoTDataset.com on Jan 05, 2026
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