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 archive.ics.uci.edu.

Scenario

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: HeartDiseaseBinary.mpx
Baseline data: HeartDiseaseCartBaseline.csv
Predict data: HeartDiseaseCartPredict.csv
Stability data: HeartDiseaseCartStability.csv

Note

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