Dataset: Breast Cancer Dataset [Wisconsin Diagnostic UCI]

This dataset is taken from the UCI Machine Learning Repository (Link: https://data.world/health/breast-cancer-wisconsin) by the Donor: Nick Street

The main idea and inspiration behind the upload was to provide datasets for Machine Learning as practice and reference for my peers at college. The main purpose is to analyze data and experiment with different machine learning ideas and techniques for this binary classification task. As such, this dataset is a very useful resource to practice on.

Breast cancer is when breast cells mutate and become cancerous cells that multiply and form tumors. It accounts for 25% of all cancer cases and affected over 2.1 Million people in 2015 alone. Breast cancer typically affects women and people assigned female at birth (AFAB) age 50 and older, but it can also affect men and people assigned male at birth (AMAB), as well as younger women. Healthcare providers may treat breast cancer with surgery to remove tumors or treatment to kill cancerous cells.

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 task: To classify whether the tumor is benign (B) or malignant (M).

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/

Number of instances: 569

Number of attributes: 32 (ID, diagnosis, 30 real-valued input features)

Original 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

Donor: Nick Street

Date: November 1995

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.

Breast Cancer Dataset [Wisconsin Diagnostic UCI]

About this Dataset

Breast Cancer Dataset [Wisconsin Diagnostic UCI]

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