Creators:
1. Dr. William H. Wolberg, General Surgery Dept. University of Wisconsin, Clinical Sciences Center Madison, WI 53792wolberg '@' eagle.surgery.wisc.edu
2. W. Nick Street, Computer Sciences Dept. University of Wisconsin, 1210 West Dayton St., Madison, WI 53706street '@' cs.wisc.edu 608-262-6619
3. Olvi L. Mangasarian, Computer Sciences Dept. University of Wisconsin, 1210 West Dayton St., Madison, WI 53706olvi '@' cs.wisc.edu
Donor:
Nick Street
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
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.educd math-prog/cpo-dataset/machine-learn/WDBC/
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)
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.
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:
Please refer to the Machine Learning Repository's citation policy. [1] Papers were automatically harvested and associated with this data set, in collaborationwith
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Breast Cancer Wisconsin (Prognostic) Data Set’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sarahvch/breast-cancer-wisconsin-prognostic-data-set on 29 August 2021.
--- Dataset description provided by original source is as follows ---
Data From: UCI Machine Learning Repository http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wpbc.names
"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/
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)"
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
I'm really interested in trying out various machine learning algorithms on some real life science data.
--- Original source retains full ownership of the source dataset ---
Prognostic Wisconsin Breast Cancer Database
http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/
Data From: UCI Machine Learning Repository http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wpbc.names
"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/
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)"
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
I'm really interested in trying out various machine learning algorithms on some real life science data.
Author:
Source: Unknown -
Please cite:
Title: Wisconsin Prognostic Breast Cancer (WPBC)
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
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)
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/
Number of instances: 198
Number of attributes: 34 (ID, outcome, 32 real-valued input features)
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
Missing attribute values: Lymph node status is missing in 4 cases.
Class distribution: 151 nonrecur, 47 recur
Luis Torgo's version: (reconstructed) - removed the four instances with unknown values of the last attribute - exchanged the attribute position of attributes n.3 (Time) and n.35 (Lymph node). - removed the attribute outcome as it is the class attribute if the
Creator:
Dr. WIlliam H. Wolberg (physician)
University of Wisconsin Hospitals
Madison, Wisconsin, USA
Donor:
Olvi Mangasarian (mangasarian '@' cs.wisc.edu)
Received by David W. Aha (aha '@' cs.jhu.edu)
Samples arrive periodically as Dr. Wolberg reports his clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself:
Group 1: 367 instances (January 1989)
Group 2: 70 instances (October 1989)
Group 3: 31 instances (February 1990)
Group 4: 17 instances (April 1990)
Group 5: 48 instances (August 1990)
Group 6: 49 instances (Updated January 1991)
Group 7: 31 instances (June 1991)
Total: 699 points (as of the donated datbase on 15 July 1992)
Note that the results summarized above in Past Usage refer to a dataset of size 369, while Group 1 has only 367 instances. This is because it originally contained 369 instances; 2 were removed. The following statements summarizes changes to the original Group 1's set of data:
Wolberg, W.H., & Mangasarian, O.L. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In Proceedings of the National Academy of Sciences, 87, 9193*9196.
Zhang, J. (1992). Selecting typical instances in instance-based learning. In Proceedings of the Ninth International Machine Learning Conference (pp. 470*479). Aberdeen, Scotland: Morgan Kaufmann.
This breast cancer databases was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. If you publish results when using this database, then please include this information in your acknowledgements. Also, please cite one or more of:1. O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.2. William H. Wolberg and O.L. Mangasarian: "Multisurface method of pattern separation for medical diagnosis applied to breast cytology", Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196.3. O. L. Mangasarian, R. Setiono, and W.H. Wolberg: "Pattern recognition via linear programming: Theory and application to medical diagnosis", in: "Large-scale numerical optimization", Thomas F. Coleman and Yuying Li, editors, SIAM Publications, Philadelphia 1990, pp 22-30.4. K. P. Bennett & O. L. Mangasarian: "Robust linear programming discrimination of two linearly inseparable sets", Optimization Methods and Software 1, 1992, 23-34 (Gordon & Breach Science
UCI Breast Cancer Raw Dataset is a breast cancer dataset that contains three sets of breast cancer cytopathology image data. Features are calculated from digitized images of fine needle aspiration (FNA) of breast masses. They describe the image The characteristics of the nuclei appearing in . The original UCI Breast Cancer dataset was published in 1995 by Dr. William H. Wolberg, General Surgery Dept. W. Nick Street, Computer Sciences Dept. Olvi L. Mangasarian, Computer Sciences Dept. Related papers are Breast cancer diagnosis and prognosis via linear programming etc.
Creators:
Matjaz Zwitter & Milan Soklic (physicians)
Institute of Oncology University Medical Center
Ljubljana, Yugoslavia
Donors:
Ming Tan and Jeff Schlimmer (Jeffrey.Schlimmer '@' a.gp.cs.cmu.edu)
This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also lymphography and primary-tumor.)
This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal.
Michalski,R.S., Mozetic,I., Hong,J., & Lavrac,N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Application to Three Medical Domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, 1041-1045, Philadelphia, PA: Morgan Kaufmann.
Clark,P. & Niblett,T. (1987). Induction in Noisy Domains. In Progress in Machine Learning (from the Proceedings of the 2nd European Working Session on Learning), 11-30, Bled, Yugoslavia: Sigma Press.
Tan, M., & Eshelman, L. (1988). Using weighted networks to represent classification knowledge in noisy domains. Proceedings of the Fifth International Conference on Machine Learning, 121-134, Ann Arbor, MI.
Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users. In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press.
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Creators:
1. Dr. William H. Wolberg, General Surgery Dept. University of Wisconsin, Clinical Sciences Center Madison, WI 53792wolberg '@' eagle.surgery.wisc.edu
2. W. Nick Street, Computer Sciences Dept. University of Wisconsin, 1210 West Dayton St., Madison, WI 53706street '@' cs.wisc.edu 608-262-6619
3. Olvi L. Mangasarian, Computer Sciences Dept. University of Wisconsin, 1210 West Dayton St., Madison, WI 53706olvi '@' cs.wisc.edu
Donor:
Nick Street
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
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.educd math-prog/cpo-dataset/machine-learn/WDBC/
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)
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.
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:
Please refer to the Machine Learning Repository's citation policy. [1] Papers were automatically harvested and associated with this data set, in collaborationwith