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)

Format

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

Details

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.

Source

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.

References

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.