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Land WH, Heine JJ, Raway T, et al. New statistical learning theory paradigms adapted to breast cancer diagnosis/classification using image and non-image clinical data. Int J Funct Inform Personal Med. 2008;1(2):111-139doi: 10.1504/ijfipm.2008.020183.
Land, W. H., Heine, J. J., Raway, T., Mizaku, A., Kovalchuk, N., Yang, J. Y., & Yang, M. Q. (2008). New statistical learning theory paradigms adapted to breast cancer diagnosis/classification using image and non-image clinical data. International journal of functional informatics and personalised medicine, 1(2), 111-139. https://doi.org/10.1504/ijfipm.2008.020183
Land, Walker H, et al. "New statistical learning theory paradigms adapted to breast cancer diagnosis/classification using image and non-image clinical data." International journal of functional informatics and personalised medicine vol. 1,2 (2008): 111-139. doi: https://doi.org/10.1504/ijfipm.2008.020183
Land WH, Heine JJ, Raway T, Mizaku A, Kovalchuk N, Yang JY, Yang MQ. New statistical learning theory paradigms adapted to breast cancer diagnosis/classification using image and non-image clinical data. Int J Funct Inform Personal Med. 2008 Jan;1(2):111-139. doi: 10.1504/ijfipm.2008.020183. PMID: 26430470; PMCID: PMC4587773.
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Int J Funct Inform Personal Med. 2008 Jan;1(2):111-139. doi: 10.1504/ijfipm.2008.020183.

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

International journal of functional informatics and personalised medicine

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

Affiliations

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

PMID: 26430470 PMCID: PMC4587773 DOI: 10.1504/ijfipm.2008.020183

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.

Keywords: computer aided diagnosis/classification; evolutionary programming/evolutionary strategies derived Support Vector Machines; kernel-partial least squares; machine intelligence

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