Title: New statistical learning theory paradigms adapted to breast cancer diagnosis/classification using image and non-image clinical data

Authors: Walker H. Land Jr., John J. Heine, Tom Raway, Alda Mizaku, Nataliya Kovalchuk, Jack Y. Yang, Mary Qu Yang

Addresses: Department of Bioengineering, Binghamton University, Binghamton, NY, 13903-6000, USA. ' Moffitt Cancer Center, University of South Florida Tampa, USA. ' Department of Bioengineering, Binghamton University, Binghamton, NY, 13903-6000, USA. ' Department of Bioengineering, Binghamton University, Binghamton, NY, 13903-6000, USA. ' Moffitt Cancer Center, University of South Florida Tampa, USA. ' Harvard Medical School, Harvard University, Cambridge, Massachusetts, 02140-0888, USA. ' National Human Genome Research Institute, National Institute of Health, US Department of Health and Human Services, Bethesda, MD 20852, USA

Abstract: The automated decision paradigms presented in this work address the false positive (FP) biopsy occurrence in diagnostic mammography. An EP/ES stochastic hybrid and two kernelized Partial Least Squares (K-PLS) paradigms were investigated with following studies: methodology performance comparisons; automated diagnostic accuracy assessments with two data sets. The findings showed: the new hybrid produced comparable results more rapidly; the new K-PLS paradigms train and operate Essentially in real time for the data sets studied. Both advancements are essential components for eventually achieving the FP reduction goal, while maintaining acceptable diagnostic sensitivities.

Keywords: kernel partial least squares; evolutionary programming; evolutionary strategies; support vector machines; SVM; machine intelligence; computer aided diagnosis; computer aided classification; statistical learning theory; breast cancer; cancer diagnosis; false positive biopsy; false positives; diagnostic mammography.

DOI: 10.1504/IJFIPM.2008.020183

International Journal of Functional Informatics and Personalised Medicine, 2008 Vol.1 No.2, pp.111 - 139

Published online: 08 Sep 2008 *

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