Breast Cancer Wisconsin (Diagnostic)

Donated on 10/31/1995

Diagnostic Wisconsin Breast Cancer Database.

Dataset Characteristics

Multivariate

Subject Area

Health and Medicine

Associated Tasks

Classification

Feature Type

Real

# Instances

569

# Features

30

Dataset Information

Additional Information

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/

Has Missing Values?

No

Introductory Paper

Nuclear feature extraction for breast tumor diagnosis

By W. Street, W. Wolberg, O. Mangasarian. 1993

Published in Electronic imaging

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
IDIDCategoricalno
DiagnosisTargetCategoricalno
radius1FeatureContinuousno
texture1FeatureContinuousno
perimeter1FeatureContinuousno
area1FeatureContinuousno
smoothness1FeatureContinuousno
compactness1FeatureContinuousno
concavity1FeatureContinuousno
concave_points1FeatureContinuousno

0 to 10 of 32

Additional Variable Information

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)

Baseline Model Performance

Papers Citing this Dataset

Machine learning in medicine: a practical introduction

By Jenni Sidey-Gibbons, Chris Sidey-Gibbons. 2019

Published in BMC medical research methodology.

Data Augmentation Using GANs

By Fabio Tanaka, Claus Aranha. 2019

Published in ArXiv.

Interpretable Counterfactual Explanations Guided by Prototypes

By Arnaud Looveren, Janis Klaise. 2019

Published in ArXiv.

PIDT: A Novel Decision Tree Algorithm Based on Parameterised Impurities and Statistical Pruning Approaches

By Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Doina Logofatu, Alexander Zamyatin. 2018

Published in AIAI.

0 to 5 of 37

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37 citations
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Keywords

healthcancer

Creators

William Wolberg

Olvi Mangasarian

Nick Street

W. Street

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