wdbc
OpenML dataset with id 1510
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/1592318/wdbc.arff
Upload date: 26 May 2015
Dataset Characteristics
Number of classes: 2
Number of features: 31 (numeric: 30, symbolic: 1 and in total binary: 1 )
Number of instances: 569
Number of instances with missing values: 0
Number of missing values: 0
Author: William H. Wolberg, W. Nick Street, Olvi L. Mangasarian Source: UCI), University of Wisconsin - 1995 Please cite: UCI
Breast Cancer Wisconsin (Diagnostic) Data Set (WDBC). 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. The target feature records the prognosis (benign (1) or malignant (2)). Original data available here
Current dataset was adapted to ARFF format from the UCI version. Sample code ID's were removed.
! Note that there is also a related Breast Cancer Wisconsin (Original) Data Set with a different set of features, better known as breast-w.
Feature description
Ten real-valued features are computed for each of 3 cell nuclei, yielding a total of 30 descriptive features. See the papers below for more details on how they were computed. The 10 features (in order) are:
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)
Relevant Papers
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.
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