This dataset contains information concerning heart disease diagnosis. The data was collected from the Cleveland Clinic Foundation, and it is available at the UCI machine learning Repository. Six instances containing missing values.
A data frame with 297 observations on the following 14 variables.
cp, chest pain type:1,2,3,4
trestbps: resting blood pressure(continuous)
fps: fatsing blood sugar>120? yes=1, no =0
restecg: resting electrocardiographic results, 0,1, 2
thalach: maximum heart rate achieved(continuous)
exang: exercise induced angina (1 = yes; 0 = no)
oldpeak = ST depression induced by exercise relative to rest (continuous)
slope: the slope of the peak exercise ST segment
ca: number of major vessels (0-3) colored by flourosopy
thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
diagnosis of heart disease: 1: < 50 2: > 50
This dataset contains six instances having missing values. It is recommended to impute these values before applying other tasks. This dataset includes continuous, binomial, nominal, and ordinal features.
The UCI Machine Learning Database Repository at:
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Warning messages: 1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display 2: 'rgl_init' failed, running with rgl.useNULL = TRUE 3: .onUnload failed in unloadNamespace() for 'rgl', details: call: fun(...) error: object 'rgl_quit' not found Report on missing values for heart-Cleveland : Number of missing values overall: 6 Percent of missing values overall: 0.1523229 Features with missing values (percent): V12 V13 1.320132 0.660066 Percent of features with missing values: 15.38462 Number of instances with missing values: 6 Percent of instances with missing values: 1.980198
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