Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics
The dataset I have used in this repo is publicly available and was created by Dr. William H. Wolberg, physician at the University Of Wisconsin Hospital at Madison, Wisconsin, USA. link: http://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28diagnostic%29
Ten real-valued features are computed for each cell nucleus:
radius (mean of distances from center to points on the perimeter)
texture (standard deviation of gray-scale values)
perimeter
area
smoothness
compactness
concavity
concave points (number of concave portions of the contour)
symmetry
fractal dimension
Through this features one can predict the tumor is either Malignant(cancer causing) or Benign(normal tumor) using machine learning
The algorithm that i have used is Support Vector Machines which have 97.2% accuracy in prediction.