# Content
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at [Web Link]
Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree Construction Via Linear Programming.\" Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.
# Authors
Dr. William H. Wolberg, General Surgery Dept.
University of Wisconsin, Clinical Sciences Center
Madison, WI 53792
wolberg '@' eagle.surgery.wisc.edu
W. Nick Street, Computer Sciences Dept.
University of Wisconsin, 1210 West Dayton St., Madison, WI 53706
street '@' cs.wisc.edu 608-262-6619
Olvi L. Mangasarian, Computer Sciences Dept.
University of Wisconsin, 1210 West Dayton St., Madison, WI 53706
olvi '@' cs.wisc.edu
# Attributes
1) ID number
2) Diagnosis (M = malignant, B = benign)
3-32)
Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension (\"coastline approximation\" - 1)
# Source
http://mlr.cs.umass.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

# Wisconsin Breast Cancer Diagnostics

## Files

*122KB*

## License

CC BY-NC-SA 4.0

## Datasets

### Wisconsin Breast Cancer Diagnosticswisconsin-breast-cancer

**Id**

**Diagnosis**

**Radius (Mean)**

**Texture (Mean)**

**Perimeter (Mean)**

**Area (Mean)**

**Smoothness (Mean)**

**Compactness (Mean)**

**Concavity (Mean)**

**Concave Points (Mean)**

**Symmetry (Mean)**

**Fractal Dimension (Mean)**

**Radius (Standard Error)**

**Texture (Standard Error)**

**Perimeter (Standard Error)**

**Area (Standard Error)**

**Smoothness (Standard Error)**

**Compactness (Standard Error)**

**Concavity (Standard Error)**

**Concave Points (Standard Error)**

**Symmetry (Standard Error)**

**Fractal Dimension (Standard Error)**

**Radius Worst**

**Texture Worst**

**Perimeter Worst**

**Area Worst**

**Smoothness Worst**

**Compactness Worst**

**Concavity Worst**

**Concave Points Worst**

**Symmetry Worst**

**Fractal Dimension Worst**