`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.

`data(wdbc)`

A data frame with 569 observations on the following variables:

`ID`

ID number

`Diagnosis`

cancer diagnosis:

`M`

= malignant,`B`

= benign`Radius_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

The recorded features are:

`Radius`

as mean of distances from center to points on the perimeter`Texture`

as standard deviation of gray-scale values`Perimeter`

as cell nucleus perimeter`Area`

as cell nucleus area`Smoothness`

as local variation in radius lengths`Compactness`

as cell nucleus compactness, perimeter^2 / area - 1`Concavity`

as severity of concave portions of the contour`Nconcave`

as number of concave portions of the contour`Symmetry`

as cell nucleus shape`Fractaldim`

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.

Breast Cancer Wisconsin (Diagnostic) Data Set (`wdbc.data`

, `wdbc.names`

) is available at UCI Machine Learning Repository
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic). Please note the UCI conditions of use.

Mangasarian, O. L., Street, W. N., and Wolberg, W. H. (1995) Breast cancer diagnosis and prognosis via linear programming. *Operations Research*, 43(4), pp. 570-577.