I am currently involved in analyzing a particular dataset called Haberman Survival Dataset. The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago's Billings Hospital on the survival of patients who had undergone surgery for breast cancer.
- Age of patient at time of operation (numerical)
- Patient's year of operation (year - 1900, numerical)
- Number of positive axillary nodes detected (numerical)
- Survival status (class attribute), 1 = the patient survived 5 years or longer, 2 = the patient died within 5 year
For this dataset I have first processed/cleaned the dataset using R, and now I am planning to collect a sample from this population and then train the sample data (training set) with some suitable machine learning algorithm and build a suitable model (1-3 are predictor variables, 4 is the response variable), and then again collect the sample (test set) from the population and then apply the model that I have built to test set and predict the outcome.
What machine learning models can be used to achieve this objective?