Data Dictionary#

The original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+Disease

There is also a version of it available on Kaggle. https://www.kaggle.com/datasets/sumaiyatasmeem/heart-disease-classification-dataset

Below is a description of the key features used in the project:

• age: Age in years

• sex: (1 = male; 0 = female)

• cp: Chest pain type

  • 0: Typical angina (chest pain related to decreased blood supply to the heart)

  • 1: Atypical angina (chest pain not related to heart disease)

  • 2: Non-anginal pain (typically esophageal spasms)

  • 3: Asymptomatic (chest pain without disease symptoms)

• trestbps: Resting blood pressure in mm Hg on admission (concern if >130-140)

• chol: Serum cholesterol in mg/dl (concern if >200)

• fbs: Fasting blood sugar > 120 mg/dl (1 = true; 0 = false)

• restecg: Resting electrocardiographic results

  • 0: Normal

  • 1: ST-T wave abnormality

  • 2: Left ventricular hypertrophy

• thalach: Maximum heart rate achieved

• exang: Exercise-induced angina (1 = yes; 0 = no)

• oldpeak: ST depression induced by exercise relative to rest

• slope: Slope of the peak exercise ST segment

  • 0: Upsloping (better heart rate with exercise)

  • 1: Flat (minimal change, typical healthy heart)

  • 2: Downsloping (unhealthy heart signs)

• ca: Number of major vessels (0-3) colored by fluoroscopy

• thal: Thalium stress test result

  • 1,3: Normal

  • 6: Fixed defect

  • 7: Reversible defect

• target: Presence of heart disease (1 = yes; 0 = no)