Models of this data predict the absence or presence of heart disease.
A data frame containing 270 observations on 14 variables.
age in years.
binary variable indicating sex.
factor variable indicating the chest pain type, with levels
non-anginal pain and
resting blood pressure.
serum cholesterol in mg/dl.
binary variable indicating if fasting blood sugar > 120 mg/dl.
factor variable indicating resting electrocardiographic results, with levels
1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) and
2: showing probable or definite left ventricular hypertrophy by Estes' criteria.
the maximum heart rate achieved.
binary variable indicating the presence of exercise induced angina.
oldpeak = ST depression induced by exercise relative to rest.
ordered factor variable describing the slope of the peak exercise ST segment, with levels
number of major vessels colored by flouroscopy.
factor variable thal, with levels
fixed defect and
binary variable indicating the
absence of heart disease.
The use of a cost matrix is suggested for this dataset. It is worse to class patients with heart disease as patients without heart disease (cost = 5), than it is to class patients without heart disease as having heart disease (cost = 1).
The dataset has been taken from the UCI Repository Of Machine Learning Databases at
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