Diagnostic attributes of patients classified as having heart disease or not.
Format
270 observations from 17 variables represented as a list consisting
of a binary factor response vector y,
with levels 'absence' and 'presence' indicating the absence or presence of
heart disease and x: a sparse feature matrix of class 'dgCMatrix' with the
following variables:
- age
age
- bp
diastolic blood pressure
- chol
serum cholesterol in mg/dl
- hr
maximum heart rate achieved
- old_peak
ST depression induced by exercise relative to rest
- vessels
the number of major blood vessels (0 to 3) that were colored by fluoroscopy
- sex
sex of the participant: 0 for male, 1 for female
- angina
a dummy variable indicating whether the person suffered angina-pectoris during exercise
- glucose_high
indicates a fasting blood sugar over 120 mg/dl
- cp_typical
typical angina
- cp_atypical
atypical angina
- cp_nonanginal
non-anginal pain
- ecg_abnormal
indicates a ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
- ecg_estes
probable or definite left ventricular hypertrophy by Estes' criteria
- slope_flat
a flat ST curve during peak exercise
- slope_downsloping
a downwards-sloping ST curve during peak exercise
- thal_reversible
reversible defect
- thal_fixed
fixed defect
Source
Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository http://archive.ics.uci.edu/ml/. Irvine, CA: University of California, School of Information and Computer Science.