This breast cancer database was obtained from the University of Wisconsin Hospitals, Madison from Dr William H. Wolberg’s office. Each record represents follow-up data for one breast cancer case. These are consecutive patients seen by Dr. Wolberg since 1984, and include only those cases exhibiting invasive breast cancer and no evidence of distant metastases at the time of diagnosis.
This dataset contains 3 measures (mean, standard deviation, and worst) for 10 different cell features - radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension. All these features have been computed from a digitized image of a fine needle aspirate of a breast mass, and they describe characteristics of the cell nuclei present in the image.
Then, the tumor size represents the diameter of excised tumor in centimeters, and lymph node status indicates the number of positive axillary lymph nodes. Outcome column represents whether the tumor in a given patient was recurrent (R) or nonrecurrent (N). Time represents the recurrence time in outcome is recurrent (R), and disease-free time if the outcome is non-recurrent (N).
Analyse this dataset to answer the following questions.
a. Build a classifier to predict the outcome of a new patient with high accuracy. Also, remember
that as a data-scientist working on healthcare problems, your intent should also be to minimize
the number of false-negatives.
b. Build a regression model to predict the recurrence time for patients whose outcome is R.