Heart-Statlog-UCI: Clinical Data for Heart Disease Diagnosis

The Heart-Statlog-UCI dataset is a collection of clinical attributes for predicting the presence or absence of heart disease. It was contributed by Asuncion and Newman from the UCI Machine Learning Repository. The dataset includes 13 attributes such as age, chest pain type, and maximum heart rate, with a mix of real, ordered, binary, and nominal data types.

Use Cases

• Predicting heart disease presence based on clinical attributes like age, sex, and chest pain type.
• Training binary classifiers using features such as resting blood pressure and serum cholesterol.
• Analyzing the relationship between exercise-induced angina and heart disease diagnosis.
• Benchmarking feature importance for medical diagnosis using variables like thalassemia and ST segment slope.

Strengths

• Includes 13 clinically relevant attributes per patient record.
• Attributes are clearly categorized by data type (real, ordered, binary, nominal).
• Target variable is explicitly defined as the absence or presence of heart disease.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

UCI Machine Learning Repository, authors Asuncion, A., & Newman, D.

Collection Method

null

Time Range

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Freshness

unknown

Geography

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