Online prediction of ovarian cancer

Abstract

In this paper we apply computer learning methods to the diagnosis of ovarian cancer using the level of the standard biomarker CA125 in conjunction with information provided by mass spectrometry. Our algorithm gives probability predictions for the disease. 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. It produces p-values that are less than those produced by an algorithm that has been previously applied to this data set. Our conclusion is that the proposed algorithm is especially reliable for prediction the ovarian cancer on some stages. © 2009 Springer Berlin Heidelberg.