Predicting survival on Haberman's dataset 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.

Attribute Information:


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?
You can use any classification algorithm. Read up on Logistic Regression and Support Vector Machines, if you have sufficient data, you can try neural networks. The threshold score is of extreme importance here for good precision and recall. So lookup, precision-recall curves and ROC curves which help in determining a reasonable threshold.