Deep Learning Model on Breast Cancer Data using TensorFlow

Data:

The data can be found on UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic)

Attribute Information:

1. ID number
2. Diagnosis (M = malignant, B = benign)
3. 30 Features

Ten real-valued features are computed for each cell nucleus:

• radius (mean of distances from center to points on the perimeter)
• texture (standard deviation of gray-scale values)
• perimeter
• area
• smoothness (local variation in radius lengths)
• compactness (perimeter^2 / area - 1.0)
• concavity (severity of concave portions of the contour)
• oncave points (number of concave portions of the contour)
• symmetry
• 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