/Breast-Cancer

Predicting the probability that a diagnosed breast cancer case is malignant or benign based on Wisconsin dataset.

Primary LanguageJupyter Notebook

Exploring and evaluating different Machine Learning Algorithms for Breast Cancer Data

In this project I evaluate the use several common machine learning algorithms to classify tumors as malignant or benign, using data from Breast Cancer Wisconson dataset. I also explore different feature extraction techniques and combine them with various ML methods to obtain the highest possible accuracy.

The dataset consists of ten real-valued features obtained from images of fine needle aspirate of breast mass :

  1. radius (mean of distances from center to points on the perimeter)
  2. texture (standard deviation of gray-scale values)
  3. perimeter
  4. area
  5. smoothness (local variation in radius lengths)
  6. compactness (perimeter^2 / area - 1.0)
  7. concavity (severity of concave portions of the contour)
  8. concave points (number of concave portions of the contour)
  9. symmetry 10.fractal dimension ("coastline approximation" - 1)