breast_cancer: Wisconsin Diagnostic Breast Cancer (WDBC)
In mangoTraining: Mango Solutions Training Datasets

Description Usage Format Note Source References

The data contain measurements on cells in suspicious lumps in a women's breast. Features are computed from a digitised image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. All samples are classified as either benign or malignant.

1	breast_cancer

breast_cancer is a tibble with 22 columns. The first column is an ID column. The second indicates whether the sample is classified as benign or malignant. The remaining columns contain measurements for 20 features. Ten real-valued features are computed for each cell nucleus. The references listed below contain detailed descriptions of how these features are computed. The mean, and "worst" (or largest - mean of the three largest values) of these features were computed for each image, resulting in 20 features. Below are descriptions of these features where * should be replaced by either mean or worst.

*_radius: mean of distances from center to points on the perimeter
*_texture: standard deviation of gray-scale values
*_perimeter: perimeter value
*_area: area value
*_smoothness: local variation in radius lengths
*_compactness: perimeter^2 / area - 1.0
*_concavity: severity of concave portions of the contour
*_concave_points: number of concave portions of the contour
*_symmetry: symmetry value
*_fractal_dimension: "coastline approximation" - 1

This breast cancer database was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.

https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming",
SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18. William H. Wolberg and O.L. Mangasarian: "Multisurface method of pattern separation for medical diagnosis applied to breast cytology",
Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196. K. P. Bennett & O. L. Mangasarian: "Robust linear programming discrimination of two linearly inseparable sets",
Optimization Methods and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers).

mangoTraining documentation built on April 28, 2021, 9:07 a.m.