heartdisease: Heart disease dataset

In MLDataR: Collection of Machine Learning Datasets for Supervised Machine Learning

Heart disease dataset

Description

The dataset is to be used with a supervised classification ML model to classify heart disease.

Usage

heartdisease

Format

A data frame with 918 rows and 10 variables:

Age

age of the patient presenting with heart disease

Sex

gender of the patient

RestingBP

blood pressure for resting heart beat

Cholesterol

Cholesterol reading

FastingBS

blood sample of glucose after a patient fasts https://www.diabetes.co.uk/diabetes_care/fasting-blood-sugar-levels.html

RestingECG

Resting echocardiography is an indicator of previous myocardial infarction e.g. heart attack

MaxHR

Maximum heart rate

Angina

chest pain caused by decreased flood flow https://www.nhs.uk/conditions/angina/

HeartPeakReading

reading at the peak of the heart rate

HeartDisease

the classification label of whether patient has heart disease or not

Source

Collected by Gary Hutson [e-mail], Dec-2021

Examples

library(dplyr)
library(ConfusionTableR)
data(heartdisease)

# Convert diabetes data to factor'
hd <- heartdisease %>%
 glimpse() %>%
 mutate(HeartDisease = as.factor(HeartDisease))
# Check that the label is now a factor
 is.factor(hd$HeartDisease)
 # Dummy encoding
# Get categorical columns
hd_cat <- hd  %>%
 dplyr::select_if(is.character)
 # Dummy encode the categorical variables
 # Specify the columns to encode
 cols <- c("RestingECG", "Angina", "Sex")
 # Dummy encode using dummy_encoder in ConfusionTableR package
 coded <- ConfusionTableR::dummy_encoder(hd_cat, cols, remove_original = TRUE)
coded <- coded %>%
    select(RestingECG_ST, RestingECG_LVH, Angina=Angina_Y,
    Sex=Sex_F)
# Remove column names we have encoded from original data frame
hd_one <- hd[,!names(hd) %in% cols]
# Bind the numerical data on to the categorical data
hd_final <- bind_cols(coded, hd_one)
# Output the final encoded data frame for the ML task
glimpse(hd_final)

MLDataR documentation built on Oct. 3, 2022, 5:08 p.m.