Breast Cancer Wisconsin (Prognostic)

Donated on 11/30/1995

Prognostic Wisconsin Breast Cancer Database

Dataset Characteristics

Multivariate

Subject Area

Health and Medicine

Associated Tasks

Classification, Regression

Feature Type

Real

# Instances

198

# Features

33

Dataset Information

Additional Information

Each record represents follow-up data for one breast cancer case. These are consecutive patients seen by Dr. Wolberg since 1984, and include only those cases exhibiting invasive breast cancer and no evidence of distant metastases at the time of diagnosis. The first 30 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/ The separation 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]. The Recurrence Surface Approximation (RSA) method is a linear programming model which predicts Time To Recur using both recurrent and nonrecurrent cases. See references (i) and (ii) above for details of the RSA method. This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WPBC/

Has Missing Values?

Yes

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
IDIDIntegerno
TimeFeatureIntegerno
radius1FeatureContinuousno
texture1FeatureContinuousno
perimeter1FeatureContinuousno
area1FeatureContinuousno
smoothness1FeatureContinuousno
compactness1FeatureContinuousno
concavity1FeatureContinuousno
concave_points1FeatureContinuousno

0 to 10 of 35

Additional Variable Information

1) ID number 2) Outcome (R = recur, N = nonrecur) 3) Time (recurrence time if field 2 = R, disease-free time if field 2 = N) 4-33) 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) 34) Tumor size - diameter of the excised tumor in centimeters 35) Lymph node status - number of positive axillary lymph nodes observed at time of surgery

Class Labels

R = recur, N = nonrecur

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Creators

William Wolberg

W. Street

Olvi Mangasarian

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