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  1. Breast Cancer Wisconsin (Prognostic)

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

    • www.kaggle.com
    Updated Mar 31, 2017
  3. Breast Cancer Wisconsin (Diagnostic) Data Set

    • www.kaggle.com
    Updated Sep 25, 2016
  4. Breast Cancer WI (Diagnostic)

    • data.world
    Updated May 22, 2018
  5. Breast Cancer Wisconsin

    • data.world
    Updated Oct 9, 2019
  6. Breast Cancer

    • data.world
    Updated Sep 18, 2019
  7. Breast Cancer Wisconsin (Original)

    • data.world
    Updated Sep 22, 2019
  8. Breast Cancer Wisconsin - Data Set

    • www.kaggle.com
    Updated Jan 8, 2018
  9. o

    Human triple negative breast cancer tissues

    • www.omicsdi.org
  10. d

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    • datamed.org
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    Data from: Gene expression variation to predict 10-year survival in...

    • www.omicsdi.org
    • omictools.com
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  12. Data from: Decision Tree-Based Learning Using Multi-Attributed Lens

    • search.datacite.org
    Published 2013
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Breast Cancer Wisconsin (Diagnostic) Data Set

Predict whether the cancer is benign or malignant

  • Dataset updated Sep 25, 2016
Dataset provided by
UCI Machine Learning
License
CC BY-NC-SA 4.0https://creativecommons.org/licenses/by-nc-sa/4.0/
Available download formats from providers
zip (125204 bytes), csv (125204 bytes)
Description

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. n 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/

Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

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)

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.

Missing attribute values: none

Class distribution: 357 benign, 212 malignant

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