Feedback
11 results found
  1. Breast Cancer Wisconsin (Prognostic) Data Set

    • www.kaggle.com
    Updated Mar 31, 2017
  2. Breast Cancer Wisconsin (Prognostic)

    • data.world
    Updated Sep 14, 2019
  3. Breast Cancer Wisconsin (Diagnostic) Data Set

    • www.kaggle.com
    Updated Sep 25, 2016
  4. Breast Cancer Wisconsin

    • data.world
    Updated Oct 9, 2019
  5. Breast Cancer Wisconsin - Data Set

    • www.kaggle.com
    Updated Jan 8, 2018
  6. Breast Cancer WI (Diagnostic)

    • data.world
    Updated May 22, 2018
  7. Breast Cancer Wisconsin (Original)

    • data.world
    Updated Sep 22, 2019
  8. Breast Cancer

    • data.world
    Updated Sep 18, 2019
  9. Data from: Decision Tree-Based Learning Using Multi-Attributed Lens

    • search.datacite.org
    Published 2013
  10. Haberman's Survival Data Set

    • www.kaggle.com
    Updated Nov 30, 2016
  11. o

    Ctr9, a key subunit of PAFc, affects global estrogen signaling and drives...

    • www.omicsdi.org
    • omictools.com
    • +1more
  12. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
Facebook
Twitter
Email
Click to copy link
Link copied

Breast Cancer Wisconsin (Prognostic) Data Set

From: UCI Machine Learning Repository

86 scholarly articles cite this dataset (View in Google Scholar)
  • Dataset updated Mar 31, 2017
Authors
Sarah VCH
License
Database: Open Database, Contents: Database Contentshttp://opendatacommons.org/licenses/dbcl/1.0/
Available download formats from providers
zip (125204 bytes), csv (125204 bytes)
Description

Context

Data From: UCI Machine Learning Repository http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wpbc.names

Content

"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)"

Acknowledgements

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 

Inspiration

I'm really interested in trying out various machine learning algorithms on some real life science data.

Search
Clear search
Close search
Google apps
Main menu