  We have applied a little-known data transformation to subsets of the
Surveillance, Epidemiology, and End Results (SEER) publically available data of
the National Cancer Institute (NCI) to make it suitable input to standard
machine learning classifiers. This transformation properly treats the
right-censored data in the SEER data and the resulting Random Forest and
Multi-Layer Perceptron models predict full survival curves. Treating the 6, 12,
and 60 months points of the resulting survival curves as 3 binary classifiers,
the 18 resulting classifiers have AUC values ranging from .765 to .885. Further
evidence that the models have generalized well from the training data is
provided by the extremely high levels of agreement between the random forest
and neural network models predictions on the 6, 12, and 60 month binary
classifiers.
