  In this paper we apply computer learning methods to diagnosing ovarian cancer
using the level of the standard biomarker CA125 in conjunction with information
provided by mass-spectrometry. We are working with a new data set collected
over a period of 7 years. Using the level of CA125 and mass-spectrometry peaks,
our algorithm gives probability predictions for the disease. To estimate
classification accuracy we convert probability predictions into strict
predictions. Our algorithm makes fewer errors than almost any linear
combination of the CA125 level and one peak's intensity (taken on the log
scale). To check the power of our algorithm we use it to test the hypothesis
that CA125 and the peaks do not contain useful information for the prediction
of the disease at a particular time before the diagnosis. Our algorithm
produces $p$-values that are better than those produced by the algorithm that
has been previously applied to this data set. Our conclusion is that the
proposed algorithm is more reliable for prediction on new data.
