Breast Cancer Wisconsin Diagnostic Dataset from UCI Machine Learning Repository

Description

Biopsy features for classification of 569 malignant (cancer) and benign (not cancer) breast masses.

Usage

brca

Format

An object of class list.

Details

Features were computationally extracted from digital images of fine needle aspirate biopsy slides. Features correspond to properties of cell nuclei, such as size, shape and regularity. The mean, standard error, and worst value of each of 10 nuclear parameters is reported for a total of 30 features.

This is a classic dataset for training and benchmarking machine learning algorithms.

• y. The outcomes. A factor with two levels denoting whether a mass is malignant ("M") or benign ("B").

• x. The predictors. A matrix with the mean, standard error and worst value of each of 10 nuclear measurements on the slide, for 30 total features per biopsy:

  • radius. Nucleus radius (mean of distances from center to points on perimeter).

  • texture. Nucleus texture (standard deviation of grayscale values).

  • perimeter. Nucleus perimeter.

  • area. Nucleus area.

  • smoothness. Nucleus smoothness (local variation in radius lengths).

  • compactness. Nucleus compactness (perimeter^2/area - 1).

  • concavity, Nucleus concavity (severity of concave portions of the contour).

  • concave_pts. Number of concave portions of the nucleus contour.

  • symmetry. Nucleus symmetry.

  • fractal_dim. Nucleus fractal dimension ("coastline approximation" -1).

Source

UCI Machine Learning Repository

Examples

table(brca$y)
dim(brca$x)
head(brca$x)