Deployment scenario and data sets: Deploy a Minitab model

A team of researchers collects and publishes detailed information about factors that affect heart disease. Variables include age, sex, cholesterol levels, maximum heart rate, and more. This example is based on a public data set that gives detailed information about heart disease. The original data are from


Heart Disease is the binary response that indicates whether the patient has heart disease: Yes or No.

The following predictors are continuous predictors.
  • Age - the age of the patient
  • Rest Blood Pressure - the resting blood pressure of the patient, in mm Hg
  • Cholesterol - the serum cholesterol level of the patient, in mg/dl
  • Max Heart Rate - the maximum heart rate achieved
  • Old Peak - the ST depression induced by exercise relative to rest
The following predictors are categorical predictors.
  • Sex - the sex of the patient: Male or Female
  • Chest Pain Type - the chest pain type: 1, 2, 3, or 4
  • Fasting Blood Sugar - whether the patient fasting blood sugar > 120 mg/dl: True or False
  • Rest ECG - the resting electrocardiographic results: 0, 1, or 2
  • Exercise Angina - whether the patient has exercise-induced angina: Yes or No
  • Slope - the slope of the peak exercise ST segment: 1, 2, or 3
  • Major Vessels - the number of major vessels colored by fluoroscopy: 0, 1, 2, 3, or 4
  • Thal - the type of defect: Normal, Fixed, or Reversible

Data sets

This tutorial illustrates the deployment of a CART® Classification model that was created in Minitab® Statistical Software. Use the following links to open the Minitab project file and the CSV data sets for baseline data, predict data, and stability data.
CART® Classification model
Baseline data
Predict data
Stability data

To learn more about creating a CART® Classification model in Minitab, go to Example of CART® Classification.