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Breast-Cancer-Prediction-from-Cytopathology-Data

This is a Nerural Network created in r that produced a 96% accuracy

About The Data

The Breast Cancer (Wisconsin) Diagnosis dataset contains the diagnosis and a set of 30 features describing the characteristics of the cell nuclei present in the digitized image of a of a fine needle aspirate (FNA) of a breast mass. 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);
  • concave points (number of concave portions of the contour);
  • symmetry;
  • fractal dimension (“coastline approximation” - 1)

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