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Kaggle Prediction Competition : Use cell nuclei categories to predict breast cancer tumor.

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breast-cancer-prediction

Kaggle Prediction Competition : Use cell nuclei categories to predict breast cancer tumor.

Data Description

from : https://www.kaggle.com/c/breast-cancer/data

Attribute Details:

Name Type Description

clump_thickness integer Value range: 1-10

uniformity_of_cell_size integer Value range: 1-10

uniformity_of_cell_shape integer Value range: 1-10

marginal_adhesion integer Value range: 1-10

single_epithelial_cell_size integer Value range: 1-10

bare_nuclei integer Value range: 1-10

bland_chromatin integer Value range: 1-10

normal_nucleoli integer Value range: 1-10

mitosis integer Value range: 1-10

class integer Predictor Value: 2 for benign, 4 for malignant

LICENSE

UCI Machine Learning Repository

This breast cancer database 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 acknowledgement. Also, please cite one or more of:

O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.
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.
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.
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 Publishers).

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