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Wisconsin Breast Cancer DiagnosticsWisconsin Breast Cancer Diagnostics

sweetdata 5 months ago 1.0.0 FREE
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CC BY-NC-SA 4.0
# Content 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 [Web Link] 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. # Authors Dr. William H. Wolberg, General Surgery Dept. University of Wisconsin, Clinical Sciences Center Madison, WI 53792 wolberg '@' eagle.surgery.wisc.edu W. Nick Street, Computer Sciences Dept. University of Wisconsin, 1210 West Dayton St., Madison, WI 53706 street '@' cs.wisc.edu 608-262-6619 Olvi L. Mangasarian, Computer Sciences Dept. University of Wisconsin, 1210 West Dayton St., Madison, WI 53706 olvi '@' cs.wisc.edu # Attributes 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) # Source http://mlr.cs.umass.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

Datasets

Wisconsin Breast Cancer Diagnosticswisconsin-breast-cancer

Id Diagnosis Radius (Mean) Texture (Mean) Perimeter (Mean) Area (Mean) Smoothness (Mean) Compactness (Mean) Concavity (Mean) Concave Points (Mean) Symmetry (Mean) Fractal Dimension (Mean) Radius (Standard Error) Texture (Standard Error) Perimeter (Standard Error) Area (Standard Error) Smoothness (Standard Error) Compactness (Standard Error) Concavity (Standard Error) Concave Points (Standard Error) Symmetry (Standard Error) Fractal Dimension (Standard Error) Radius Worst Texture Worst Perimeter Worst Area Worst Smoothness Worst Compactness Worst Concavity Worst Concave Points Worst Symmetry Worst Fractal Dimension Worst

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