Commit Inicial

data/tanveer/breast-cancer-wisc-prog/wpbc.names

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+1. Title: Wisconsin Prognostic Breast Cancer (WPBC)
+
+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: December 1995
+
+3. Past Usage:
+
+	Various versions of this data have been used in the following
+	publications: 
+
+	(i) W. N. Street, O. L. Mangasarian, and W.H. Wolberg. 
+	An inductive learning approach to prognostic prediction. 
+	In A. Prieditis and S. Russell, editors, Proceedings of the
+	Twelfth International Conference on Machine Learning, pages
+	522--530, San Francisco, 1995. Morgan Kaufmann.
+
+	(ii) 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. 
+
+	(iii) 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. 
+
+	(iv) 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.
+
+	(v) W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. 
+	Computer-derived nuclear ``grade'' and breast cancer prognosis. 
+	Analytical and Quantitative Cytology and Histology, Vol. 17,
+	pages 257-264, 1995. 
+
+See also:
+	http://www.cs.wisc.edu/~olvi/uwmp/mpml.html
+	http://www.cs.wisc.edu/~olvi/uwmp/cancer.html
+
+Results:
+
+	Two possible learning problems:
+
+	1) Predicting field 2, outcome: R = recurrent, N = nonrecurrent
+	- Dataset should first be filtered to reflect a particular
+	endpoint; e.g., recurrences before 24 months = positive,
+	nonrecurrence beyond 24 months = negative.
+	- 86.3% accuracy estimated accuracy on 2-year recurrence using
+	previous version of this data.  Learning method: MSM-T (see
+	below) in the 4-dimensional space of Mean Texture, Worst Area,
+	Worst Concavity, Worst Fractal Dimension.
+
+	2) Predicting Time To Recur (field 3 in recurrent records)
+	- Estimated mean error 13.9 months using Recurrence Surface
+	Approximation. (See references (i) and (ii) above)
+
+4. Relevant information
+
+	Each record represents follow-up data for one breast cancer
+	case.  These are consecutive patients seen by Dr. Wolberg
+	since 1984, and include only those cases exhibiting invasive
+	breast cancer and no evidence of distant metastases at the
+	time of diagnosis. 
+
+	The first 30 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/
+
+	The separation 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].
+
+	The Recurrence Surface Approximation (RSA) method is a linear
+	programming model which predicts Time To Recur using both
+	recurrent and nonrecurrent cases.  See references (i) and (ii)
+	above for details of the RSA method. 
+
+	This database is also available through the UW CS ftp server:
+
+	ftp ftp.cs.wisc.edu
+	cd math-prog/cpo-dataset/machine-learn/WPBC/
+
+5. Number of instances: 198
+
+6. Number of attributes: 34 (ID, outcome, 32 real-valued input features)
+
+7. Attribute information
+
+1) ID number
+2) Outcome (R = recur, N = nonrecur)
+3) Time (recurrence time if field 2 = R, disease-free time if 
+	field 2	= N)
+4-33) 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 4 is Mean Radius, field
+14 is Radius SE, field 24 is Worst Radius.
+
+Values for features 4-33 are recoded with four significant digits.
+
+34) Tumor size - diameter of the excised tumor in centimeters
+35) Lymph node status - number of positive axillary lymph nodes
+observed at time of surgery
+
+8. Missing attribute values: 
+	Lymph node status is missing in 4 cases.
+
+9. Class distribution: 151 nonrecur, 47 recur
+