Online Prediction of Ovarian Cancer

  • Authors:

  • Fedor Zhdanov;Vladimir Vovk;Brian Burford;Dmitry Devetyarov;Ilia Nouretdinov;Alex Gammerman

  • Affiliations:

  • Computer Learning Research Centre, Department of Computer Science Royal Holloway, University of London, Egham, Surrey, UK TW20 0EX;Computer Learning Research Centre, Department of Computer Science Royal Holloway, University of London, Egham, Surrey, UK TW20 0EX;Computer Learning Research Centre, Department of Computer Science Royal Holloway, University of London, Egham, Surrey, UK TW20 0EX;Computer Learning Research Centre, Department of Computer Science Royal Holloway, University of London, Egham, Surrey, UK TW20 0EX;Computer Learning Research Centre, Department of Computer Science Royal Holloway, University of London, Egham, Surrey, UK TW20 0EX;Computer Learning Research Centre, Department of Computer Science Royal Holloway, University of London, Egham, Surrey, UK TW20 0EX

  • Venue:
  • AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
  • Year:
  • 2009

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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.