New statistical learning theory paradigms adapted to breast cancer diagnosis/classification using image and non-image clinical data. (9th September 2008)

• Record Type: Journal Article
• Authors: Land Jr., Walker H.; Heine, John J.; Raway, Tom; Mizaku, Alda; Kovalchuk, Nataliya; Yang, Jack; Yang, Mary
• Is Part Of: International journal of functional informatics and personalised medicine. Volume 1:Number 2(2008)
• Page Start: 111
• Page End: 139
• Publication Date: 2008-09-09
• Subjects: 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; Biomedical engineering -- Periodicals; Bioinformatics -- Periodicals; 610.28
• Languages: English
• ISSNs: 1756-2104

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.