heart: Heart disease
In jolars/prague: Generalized Linear Models Regularized with the Sorted L1-Norm

Description Usage Format Preprocessing Source

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

heart

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

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.

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

jolars/prague documentation built on March 4, 2020, 7:13 p.m.