title: New statistical learning theory paradigms adapted to breast cancer diagnosis/classification using image and non-image clinical data.
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 comparisonsautomated diagnostic accuracy assessments with two data sets. The findings showed: the new hybrid produced comparable results more rapidlythe 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.
authors.affiliation: Department of Bioengineering, Binghamton University, Binghamton, NY, 13903-6000, USA.
authors.forename: Walker H
authors.initials: WH
authors.lastname: Land
authors.affiliation: Moffitt Cancer Center, University of South Florida Tampa, USA.
authors.forename: John J
authors.initials: JJ
authors.lastname: Heine
authors.affiliation: Department of Bioengineering, Binghamton University, Binghamton, NY, 13903-6000, USA.
authors.forename: Tom
authors.initials: T
authors.lastname: Raway
authors.affiliation: Department of Bioengineering, Binghamton University, Binghamton, NY, 13903-6000, USA.
authors.forename: Alda
authors.initials: A
authors.lastname: Mizaku
authors.affiliation: Moffitt Cancer Center, University of South Florida Tampa, USA.
authors.forename: Nataliya
authors.initials: N
authors.lastname: Kovalchuk
authors.affiliation: Harvard Medical School, Harvard University, Cambridge, Massachusetts, 02140-0888, USA.
authors.forename: Jack Y
authors.initials: JY
authors.lastname: Yang
authors.affiliation: National Human Genome Research Institute, National Institute of Health, US Department of Health and Human Services, Bethesda, MD 20852, USA.
authors.forename: Mary Qu
authors.initials: MQ
authors.lastname: Yang
keywords: computer aided diagnosis/classification; evolutionary programming/evolutionary strategies derived Support Vector Machines; kernel-partial least squares; machine intelligence