BREAST CANCER CLASSIFICATION USING SUPPORT VECTOR MACHINES

• 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 (perimeter² / 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

Data Source: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)