Breast Cancer Wisconsin (Diagnostic)
About
Diagnostic Wisconsin Breast Cancer Database.
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 http://www.cs.wisc.edu/~street/images/
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/
Subject Area
Health and Medicine
Instances
569
Features
30
Data Types
Multivariate
Tasks
Classification
Feature Types
Continuous
Features
| Name | Role | Type | Units | Missing Values |
|---|---|---|---|---|
| ID | Id | Categorical | - | No |
| Diagnosis | Target | Categorical | - | No |
| radius1 | Feature | Continuous | - | No |
| texture1 | Feature | Continuous | - | No |
| perimeter1 | Feature | Continuous | - | No |
| area1 | Feature | Continuous | - | No |
| smoothness1 | Feature | Continuous | - | No |
| compactness1 | Feature | Continuous | - | No |
| concavity1 | Feature | Continuous | - | No |
| concave_points1 | Feature | Continuous | - | No |
| symmetry1 | Feature | Continuous | - | No |
| fractal_dimension1 | Feature | Continuous | - | No |
| radius2 | Feature | Continuous | - | No |
| texture2 | Feature | Continuous | - | No |
| perimeter2 | Feature | Continuous | - | No |
| area2 | Feature | Continuous | - | No |
| smoothness2 | Feature | Continuous | - | No |
| compactness2 | Feature | Continuous | - | No |
| concavity2 | Feature | Continuous | - | No |
| concave_points2 | Feature | Continuous | - | No |
| symmetry2 | Feature | Continuous | - | No |
| fractal_dimension2 | Feature | Continuous | - | No |
| radius3 | Feature | Continuous | - | No |
| texture3 | Feature | Continuous | - | No |
| perimeter3 | Feature | Continuous | - | No |
| area3 | Feature | Continuous | - | No |
| smoothness3 | Feature | Continuous | - | No |
| compactness3 | Feature | Continuous | - | No |
| concavity3 | Feature | Continuous | - | No |
| concave_points3 | Feature | Continuous | - | No |
| symmetry3 | Feature | Continuous | - | No |
| fractal_dimension3 | Feature | Continuous | - | No |
Introductory Paper
Nuclear feature extraction for breast tumor diagnosis
W. Street, W. Wolberg, O. Mangasarian. 1993.
Electronic imaging