Heart Disease

Diagnostic attributes of patients classified as having heart disease or not.

heart

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.

https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#heart

Preprocessing

The original dataset contained 13 variables. The nominal of these were dummycoded, removing the first category. No precise information regarding variables chest_pain, thal and ecg could be found, which explains their obscure definitions here.