heart_disease: Heart Disease data
In m-clark/noiris: Data sets

Description Usage Format Details Note Source References Examples

From the Kaggle website: This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The "goal" field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).

1	heart_disease

Data frame with 14 variables

age: age in years
sex: Male or female
chest_pain_type: typical angina, atypical angina, non-anginal pain, asymptomatic
resting_bp: resting blood pressure (in mm Hg on admission to the hospital)
cholesterol: serum cholestoral in mg/dl
fasting_blood_sugar: blood sugar > 120 mg/dl vs. less than ('lt_120', 'gt_120')
resting_ecg: resting electrocardiographic results
max_heartrate: maximum heart rate achieved
exer_angina: exercise induced angina (yes or no)
old_peak: ST depression induced by exercise relative to rest. See this for explanation of ST.
slope: the slope of the peak exercise ST segment: positive flat or negative. See this for explanation of ST.
n_vessels: number of major vessels (0-3) colored by flourosopy
defect: normal, fixed defect, or reversible defect
heart_disease: 1 = yes, 0 = no. Left as numeric.

This data is useful for standard classification/survival (if using age).

This is the classic heart disease data only prepped for actual use and with more useful names/labels where possible. For reference the original names are: age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal, target.

The values are sometimes labeled 1:4 while the actual data values are 0-3 and similar. Assumptions have been made that these coincide as one would expect. Thal describes values of 3, 6, 7 but was actually coded 0-3 with only two zero values. The zeros were converted to NA.

Data link

Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64,304–310.

David W. Aha & Dennis Kibler. "Instance-based prediction of heart-disease presence with the Cleveland database."

Gennari, J.H., Langley, P, & Fisher, D. (1989). Models of incremental concept formation. Artificial Intelligence, 40, 11-61.