Commit Inicial

data/tanveer/breast-cancer-wisc-diag/wdbc.names

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+1. Title: Wisconsin Diagnostic Breast Cancer (WDBC)
+
+2. Source Information
+
+a) Creators: 
+
+	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 
+
+b) Donor: Nick Street
+
+c) Date: November 1995
+
+3. Past Usage:
+
+first usage:
+
+	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.
+
+OR literature:
+
+	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.
+
+Medical literature:
+
+	W.H. Wolberg, W.N. Street, and O.L. Mangasarian. 
+	Machine learning techniques to diagnose breast cancer from
+	fine-needle aspirates.  
+	Cancer Letters 77 (1994) 163-171.
+
+	W.H. Wolberg, W.N. Street, and O.L. Mangasarian. 
+	Image analysis and machine learning applied to breast cancer
+	diagnosis and prognosis.  
+	Analytical and Quantitative Cytology and Histology, Vol. 17
+	No. 2, pages 77-87, April 1995. 
+
+	W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. 
+	Computerized breast cancer diagnosis and prognosis from fine
+	needle aspirates.  
+	Archives of Surgery 1995;130:511-516.
+
+	W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. 
+	Computer-derived nuclear features distinguish malignant from
+	benign breast cytology.  
+	Human Pathology, 26:792--796, 1995.
+
+See also:
+	http://www.cs.wisc.edu/~olvi/uwmp/mpml.html
+	http://www.cs.wisc.edu/~olvi/uwmp/cancer.html
+
+Results:
+
+	- predicting field 2, diagnosis: B = benign, M = malignant
+	- sets are linearly separable using all 30 input features
+	- best predictive accuracy obtained using one separating plane
+		in the 3-D space of Worst Area, Worst Smoothness and
+		Mean Texture.  Estimated accuracy 97.5% using repeated
+		10-fold crossvalidations.  Classifier has correctly
+		diagnosed 176 consecutive new patients as of November
+		1995. 
+
+4. Relevant 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/
+
+5. Number of instances: 569 
+
+6. Number of attributes: 32 (ID, diagnosis, 30 real-valued input features)
+
+7. Attribute 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)
+
+Several of the papers listed above contain detailed descriptions of
+how these features are computed. 
+
+The mean, standard error, and "worst" or largest (mean of the three
+largest values) of these features were computed for each image,
+resulting in 30 features.  For instance, field 3 is Mean Radius, field
+13 is Radius SE, field 23 is Worst Radius.
+
+All feature values are recoded with four significant digits.
+
+8. Missing attribute values: none
+
+9. Class distribution: 357 benign, 212 malignant