HeartDisease: Mixed data : Cleveland Heart Disease Data

In MixAll: Clustering and Classification using Model-Based Mixture Models

Description

The Cleveland Heart Disease Data found in the UCI machine learning repository consists of 14 variables measured on 303 individuals who have heart disease. The individuals had been grouped into five levels of heart disease. The information about the disease status is in the HeartDisease.target data set.

Format

Three data frames with 303 observations on the following 14 variables.

age

age in years

sex

sex (1 = male; 0 = female)

cp

chest pain type. 1: typical angina, 2: atypical angina, 3: non-anginal pain, 4: asymptomatic

trestbps

resting blood pressure (in mm Hg on admission to the hospital)

chol

serum cholestoral in mg/dl

fbs

(fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)

restecg

resting electrocardiographic results. 0: normal, 1: having ST-T wave abnormality (T wave inversions and/or ST, elevation or depression of > 0.05 mV) 2: showing probable or definite left ventricular hypertrophy by 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

the slope of the peak exercise ST segment 1: upsloping, 2: flat, 3: downsloping

ca

number of major vessels (0-3) colored by flourosopy (4 missing values)

thal

3 = normal; 6 = fixed defect; 7 = reversable defect (2 missing values)

num

diagnosis of heart disease (angiographic disease status). 0: < 50 1: > 50 (in any major vessel: attributes 59 through 68 are vessels)

Details

The variables consist of five continuous and eight discrete attributes, the former in the HeartDisease.cont data set and the later in the HeartDisease.cat data set. Three of the discrete attributes have two levels, three have three levels and two have four levels. There are six missing values in the data set.

Source

Author: David W. Aha (aha 'AT' ics.uci.edu) [phone]

Donors: The data was collected from the Cleveland Clinic Foundation (cleveland.data)

https://archive.ics.uci.edu/ml/datasets/Heart+Disease

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.

Examples

summary(data(HeartDisease.cat))
summary(data(HeartDisease.cont))
summary(data(HeartDisease.target))

Example output

Loading required package: rtkore
Loading required package: Rcpp

Attaching package: 'rtkore'

The following object is masked from 'package:Rcpp':

    LdFlags

   Length     Class      Mode 
        1 character character 
   Length     Class      Mode 
        1 character character 
   Length     Class      Mode 
        1 character character 

MixAll documentation built on Sept. 12, 2019, 5:05 p.m.