Abstract
A linear support vector machine (SVM) is used to extract 6 features from a total of 31 features in a dataset of 253 breast cancer patients. Five features are nuclear features obtained during a non-invasive diagnostic procedure while one feature, tumor size, is obtained during surgery. The linear SVM selected the 6 features in the process of classifying the patients into node-positive (patients with some metastasized lymph nodes) and nodenegative (patients with no metastasized lymph nodes). Node-positive patients are typically those who undergo chemotherapy. The 6 features were then used in a Gaussian kernel nonlinear SVM to classify the patients into three prognostic groups: good (node-negative), intermediate (1 to 4 metastasized nodes) and poor (more than 4 metastasized nodes). Very well separated Kaplan-Meier survival curves were constructed for the three groups with pairwise p-value of less than 0.009 based on the logrank statistic. Patients in the good prognostic group had the highest survival, while patients in the poor prognostic group had the lowest. The majority (72.8%) of the good group did not receive chemotherapy, while the majority (87.5%) of the poor group received chemotherapy. Just over half (56.7%) of the intermediate group received chemotherapy. New patients can be assigned to one of these three prognostic groups with its associated survival curve, based only on 6 features obtained before and during surgery, but without the potentially risky procedure of removing lymph nodes to determine how many of them have metastasized.