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:
IDID number
Diagnosiscancer diagnosis: M = malignant, B = benign
Radius_meana numeric vector
Texture_meana numeric vector
Perimeter_meana numeric vector
Area_meana numeric vector
Smoothness_meana numeric vector
Compactness_meana numeric vector
Concavity_meana numeric vector
Nconcave_meana numeric vector
Symmetry_meana numeric vector
Fractaldim_meana numeric vector
Radius_sea numeric vector
Texture_sea numeric vector
Perimeter_sea numeric vector
Area_sea numeric vector
Smoothness_sea numeric vector
Compactness_sea numeric vector
Concavity_sea numeric vector
Nconcave_sea numeric vector
Symmetry_sea numeric vector
Fractaldim_sea numeric vector
Radius_extremea numeric vector
Texture_extremea numeric vector
Perimeter_extremea numeric vector
Area_extremea numeric vector
Smoothness_extremea numeric vector
Compactness_extremea numeric vector
Concavity_extremea numeric vector
Nconcave_extremea numeric vector
Symmetry_extremea numeric vector
Fractaldim_extremea 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.
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.