heartdisease: Heart Disease Data (UCI, Four Sites, with Real Missing Values)

Description

A real, deliberately messy clinical dataset: 920 patients assessed for coronary artery disease at four institutions (Cleveland, Hungary, Switzerland and the Long Beach VA). Unlike the simulated teaching sets in datarium, it ships close to raw --- integer-coded categorical variables, substantial and uneven missingness across sites, and a multi-level outcome --- so it can be used to teach the "clean the data before you analyse it" step ahead of logistic regression, chi-square tests and group comparisons. The values are the original UCI codes and are not relabelled; see the data-raw/heartdisease/heart-disease.names codebook.

For a worked tutorial on this analysis, see the Datanovia lesson “Logistic Regression in R: Model a Binary Outcome” (https://www.datanovia.com/learn/biostatistics/regression/logistic-regression-in-r).

Usage

data("heartdisease")

Arguments

Format

A data frame with 920 rows and 15 columns. The missing marker "?" in the source has been converted to NA; missingness is heavy and very uneven across sites (see colSums(is.na(heartdisease))).

age: age, in years.
sex: sex, integer-coded: 1 = male, 0 = female.
cp: chest pain type: 1 = typical angina, 2 = atypical angina, 3 = non-anginal pain, 4 = asymptomatic.
trestbps: resting blood pressure (mm Hg on admission to hospital).
chol: serum cholesterol (mg/dl). Note: 172 patients are recorded with 0 --- all 123 of the Switzerland site's patients and a further 49 at the Long Beach VA site --- where 0 means "not measured" rather than a true value: a real missing-data trap to handle before analysis.
fbs: fasting blood sugar > 120 mg/dl: 1 = true, 0 = false.
restecg: resting electrocardiographic results: 0 = normal, 1 = ST-T wave abnormality, 2 = probable or definite left ventricular hypertrophy (Estes' criteria).
thalach: maximum heart rate achieved.
exang: exercise-induced angina: 1 = yes, 0 = no.
oldpeak: ST depression induced by exercise relative to rest.
slope: slope of the peak exercise ST segment: 1 = upsloping, 2 = flat, 3 = downsloping.
ca: number of major vessels (0--3) coloured by fluoroscopy.
thal: thallium stress-test result: 3 = normal, 6 = fixed defect, 7 = reversible defect.
num: diagnosis of heart disease (angiographic disease status): 0 = < 50% diameter narrowing (no disease); 1--4 = > 50% narrowing (disease present). It is commonly binarised to absence (0) vs presence (> 0).
dataset: the collecting site, a factor with levels "cleveland", "hungarian", "switzerland" and "va".

References

Janosi, A., Steinbrunn, W., Pfisterer, M., & Detrano, R. (1989). Heart Disease [Dataset]. UCI Machine Learning Repository. tools:::Rd_expr_doi("10.24432/C52P4X").

Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J. J., Sandhu, S., Guppy, K. H., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. The American Journal of Cardiology, 64(5), 304--310.

Examples

Run this code

data("heartdisease")

# Real data is messy: missingness is heavy and very uneven across sites
colSums(is.na(heartdisease))
table(heartdisease$dataset)

# The Cleveland site is the most complete; use it for a first model
cleveland <- subset(heartdisease, dataset == "cleveland")

# Binarise the 0-4 angiographic outcome into disease present/absent
cleveland$disease <- as.integer(cleveland$num > 0)

# Logistic regression of disease on age, sex and maximum heart rate
fit <- glm(disease ~ age + sex + thalach, family = binomial, data = cleveland)
summary(fit)

# Chi-square test: is disease associated with sex (all sites)?
heartdisease$disease <- as.integer(heartdisease$num > 0)
chisq.test(table(heartdisease$sex, heartdisease$disease))