• 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

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

• “Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges.

• So the conclusion drawn from the experiment can be summed up into the following :