R: Wisconsin diagnostic breast cancer (WDBC) data

wdbc {mclust} | R Documentation

## Wisconsin diagnostic breast cancer (WDBC) data

### Description

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` = 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.

[Package mclust version 5.4.9 Index]