Case Study: Breast Cancer Classification

Predicted if the cancer diagnosis is benign or malignant based on several observations/features.

30 features of tumor are used, examples:
– 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)

Breast cancer tumor classification - malignant or benign
Breast cancer tumor -radius and smoothness (Orange=Malignant tumor, Blue=Benign tumor)

Model accuracy: Confusion Matrix

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