UCI Wisconsin Diagnostic Breast Cancer Data
wdbc.Rd
The data set provides data for 569 patients on 30 features of the cell nuclei obtained from a digitized image of a fine needle aspirate (FNA) of a breast mass. For each patient the cancer was diagnosed as malignant or benign.
Usage
data(wdbc)
Format
A data frame with 569 observations on the following variables:
ID
ID number
Diagnosis
cancer diagnosis:
M
= malignant,B
= benignRadius_mean
a numeric vector
Texture_mean
a numeric vector
Perimeter_mean
a numeric vector
Area_mean
a numeric vector
Smoothness_mean
a numeric vector
Compactness_mean
a numeric vector
Concavity_mean
a numeric vector
Nconcave_mean
a numeric vector
Symmetry_mean
a numeric vector
Fractaldim_mean
a numeric vector
Radius_se
a numeric vector
Texture_se
a numeric vector
Perimeter_se
a numeric vector
Area_se
a numeric vector
Smoothness_se
a numeric vector
Compactness_se
a numeric vector
Concavity_se
a numeric vector
Nconcave_se
a numeric vector
Symmetry_se
a numeric vector
Fractaldim_se
a numeric vector
Radius_extreme
a numeric vector
Texture_extreme
a numeric vector
Perimeter_extreme
a numeric vector
Area_extreme
a numeric vector
Smoothness_extreme
a numeric vector
Compactness_extreme
a numeric vector
Concavity_extreme
a numeric vector
Nconcave_extreme
a numeric vector
Symmetry_extreme
a numeric vector
Fractaldim_extreme
a numeric vector
Details
The recorded features are:
Radius
as mean of distances from center to points on the perimeterTexture
as standard deviation of gray-scale valuesPerimeter
as cell nucleus perimeterArea
as cell nucleus areaSmoothness
as local variation in radius lengthsCompactness
as cell nucleus compactness, perimeter^2 / area - 1Concavity
as severity of concave portions of the contourNconcave
as number of concave portions of the contourSymmetry
as cell nucleus shapeFractaldim
as fractal dimension, "coastline approximation" - 1
For each feature the recorded values are computed from each image as <feature_name>_mean
, <feature_name>_se
, and <feature_name>_extreme
, for the mean, the standard error, and the mean of the three largest values.
Source
The Breast Cancer Wisconsin (Diagnostic) Data Set (wdbc.data
, wdbc.names
) from the UCI Machine Learning Repository
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic). Please note the UCI conditions of use.