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Breast cancer wisconsin (diagnostic) dataset
Data Set Characteristics:


Number of Instances:

569


Number of Attributes:

30 numeric, predictive attributes and the class


Attribute Information:


radius (mean of distances from center to points on the perimeter)
texture (standard deviation of gray-scale values)
perimeter
area
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
fractal dimension ("coastline approximation" - 1)

The mean, standard error, and "worst" or largest (mean of the three
worst/largest values) of these features were computed for each image,
resulting in 30 features.  For instance, field 0 is Mean Radius, field
10 is Radius SE, field 20 is Worst Radius.


class:


WDBC-Malignant
WDBC-Benign


Summary Statistics:


radius (mean):
6.981
28.11


texture (mean):
9.71
39.28


perimeter (mean):
43.79
188.5


area (mean):
143.5
2501.0


smoothness (mean):
0.053
0.163


compactness (mean):
0.019
0.345


concavity (mean):
0.0
0.427


concave points (mean):
0.0
0.201


symmetry (mean):
0.106
0.304


fractal dimension (mean):
0.05
0.097


radius (standard error):
0.112
2.873


texture (standard error):
0.36
4.885


perimeter (standard error):
0.757
21.98


area (standard error):
6.802
542.2


smoothness (standard error):
0.002
0.031


compactness (standard error):
0.002
0.135


concavity (standard error):
0.0
0.396


concave points (standard error):
0.0
0.053


symmetry (standard error):
0.008
0.079


fractal dimension (standard error):
0.001
0.03


radius (worst):
7.93
36.04


texture (worst):
12.02
49.54


perimeter (worst):
50.41
251.2


area (worst):
185.2
4254.0


smoothness (worst):
0.071
0.223


compactness (worst):
0.027
1.058


concavity (worst):
0.0
1.252


concave points (worst):
0.0
0.291


symmetry (worst):
0.156
0.664


fractal dimension (worst):
0.055
0.208


Missing Attribute Values:
None


Class Distribution:
212 - Malignant, 357 - Benign


Creator:
Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian


Donor:
Nick Street


Date:
November, 1995


This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.
https://goo.gl/U2Uwz2
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.
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.
The actual linear program used to obtain the separating plane
in the 3-dimensional space is that described in:
[K. P. Bennett and O. L. Mangasarian: "Robust Linear
Programming Discrimination of Two Linearly Inseparable Sets",
Optimization Methods and Software 1, 1992, 23-34].
This database is also available through the UW CS ftp server:
ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/

References

W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction
for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on
Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
San Jose, CA, 1993.
O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and
prognosis via linear programming. Operations Research, 43(4), pages 570-577,
July-August 1995.
W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)
163-171.