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140 lines
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Plaintext
140 lines
4.6 KiB
Plaintext
1. Title: Wisconsin Diagnostic Breast Cancer (WDBC)
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2. Source Information
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a) Creators:
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Dr. William H. Wolberg, General Surgery Dept., University of
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Wisconsin, Clinical Sciences Center, Madison, WI 53792
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wolberg@eagle.surgery.wisc.edu
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W. Nick Street, Computer Sciences Dept., University of
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Wisconsin, 1210 West Dayton St., Madison, WI 53706
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street@cs.wisc.edu 608-262-6619
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Olvi L. Mangasarian, Computer Sciences Dept., University of
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Wisconsin, 1210 West Dayton St., Madison, WI 53706
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olvi@cs.wisc.edu
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b) Donor: Nick Street
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c) Date: November 1995
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3. Past Usage:
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first usage:
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W.N. Street, W.H. Wolberg and O.L. Mangasarian
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Nuclear feature extraction for breast tumor diagnosis.
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IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science
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and Technology, volume 1905, pages 861-870, San Jose, CA, 1993.
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OR literature:
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O.L. Mangasarian, W.N. Street and W.H. Wolberg.
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Breast cancer diagnosis and prognosis via linear programming.
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Operations Research, 43(4), pages 570-577, July-August 1995.
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Medical literature:
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W.H. Wolberg, W.N. Street, and O.L. Mangasarian.
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Machine learning techniques to diagnose breast cancer from
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fine-needle aspirates.
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Cancer Letters 77 (1994) 163-171.
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W.H. Wolberg, W.N. Street, and O.L. Mangasarian.
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Image analysis and machine learning applied to breast cancer
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diagnosis and prognosis.
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Analytical and Quantitative Cytology and Histology, Vol. 17
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No. 2, pages 77-87, April 1995.
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W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian.
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Computerized breast cancer diagnosis and prognosis from fine
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needle aspirates.
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Archives of Surgery 1995;130:511-516.
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W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian.
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Computer-derived nuclear features distinguish malignant from
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benign breast cytology.
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Human Pathology, 26:792--796, 1995.
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See also:
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http://www.cs.wisc.edu/~olvi/uwmp/mpml.html
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http://www.cs.wisc.edu/~olvi/uwmp/cancer.html
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Results:
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- predicting field 2, diagnosis: B = benign, M = malignant
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- sets are linearly separable using all 30 input features
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- best predictive accuracy obtained using one separating plane
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in the 3-D space of Worst Area, Worst Smoothness and
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Mean Texture. Estimated accuracy 97.5% using repeated
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10-fold crossvalidations. Classifier has correctly
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diagnosed 176 consecutive new patients as of November
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1995.
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4. Relevant information
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Features are computed from a digitized image of a fine needle
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aspirate (FNA) of a breast mass. They describe
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characteristics of the cell nuclei present in the image.
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A few of the images can be found at
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http://www.cs.wisc.edu/~street/images/
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Separating plane described above was obtained using
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Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
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Construction Via Linear Programming." Proceedings of the 4th
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Midwest Artificial Intelligence and Cognitive Science Society,
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pp. 97-101, 1992], a classification method which uses linear
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programming to construct a decision tree. Relevant features
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were selected using an exhaustive search in the space of 1-4
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features and 1-3 separating planes.
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The actual linear program used to obtain the separating plane
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in the 3-dimensional space is that described in:
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[K. P. Bennett and O. L. Mangasarian: "Robust Linear
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Programming Discrimination of Two Linearly Inseparable Sets",
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Optimization Methods and Software 1, 1992, 23-34].
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This database is also available through the UW CS ftp server:
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ftp ftp.cs.wisc.edu
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cd math-prog/cpo-dataset/machine-learn/WDBC/
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5. Number of instances: 569
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6. Number of attributes: 32 (ID, diagnosis, 30 real-valued input features)
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7. Attribute information
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1) ID number
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2) Diagnosis (M = malignant, B = benign)
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3-32)
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Ten real-valued features are computed for each cell nucleus:
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a) radius (mean of distances from center to points on the perimeter)
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b) texture (standard deviation of gray-scale values)
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c) perimeter
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d) area
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e) smoothness (local variation in radius lengths)
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f) compactness (perimeter^2 / area - 1.0)
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g) concavity (severity of concave portions of the contour)
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h) concave points (number of concave portions of the contour)
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i) symmetry
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j) fractal dimension ("coastline approximation" - 1)
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Several of the papers listed above contain detailed descriptions of
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how these features are computed.
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The mean, standard error, and "worst" or largest (mean of the three
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largest values) of these features were computed for each image,
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resulting in 30 features. For instance, field 3 is Mean Radius, field
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13 is Radius SE, field 23 is Worst Radius.
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All feature values are recoded with four significant digits.
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8. Missing attribute values: none
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9. Class distribution: 357 benign, 212 malignant |