Predicting if the cancer diagnosis is benign or malignant based on several observations/features 30 features 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)
Datasets are linearly separable using all 30 input features
Number of Instances: 569
Class Distribution: 212 Malignant, 357 Benign
Target class:
- Malignant
- Benign