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A subset polynomial neural networks approach for breast cancer diagnosis

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dc.contributor.authorO'Neill, Terence
dc.contributor.authorPenm, Jack HW
dc.contributor.authorPenm, Jonathan
dc.date.accessioned2015-12-07T22:47:38Z
dc.date.issued2007
dc.date.updated2015-12-07T11:52:55Z
dc.description.abstractBreast cancer is a very common and serious cancer for women that is diagnosed in one of every eight Australian women before the age of 85. The conventional method of breast cancer diagnosis is mammography. However, mammography has been reported to have poor diagnostic capability. In this paper we have used subset polynomial neural network techniques in conjunction with fine needle aspiration cytology to undertake this difficult task of predicting breast cancer. The successful findings indicate that adoption of NNs is likely to lead to increased survival of women with breast cancer, improved electronic healthcare, and enhanced quality of life.
dc.identifier.issn1741-8453
dc.identifier.urihttp://hdl.handle.net/1885/26144
dc.publisherInderscience Publishers
dc.sourceInternational Journal Electronic Healthcare
dc.subjectKeywords: article; artificial neural network; aspiration cytology; Australia; breast cancer; cancer classification; cancer diagnosis; cancer survival; diagnostic accuracy; mammography; quality of life; Biopsy, Fine-Needle; Breast Neoplasms; Humans; Models, Biologic Breast cancer; Classification; Healthcare improvements; Subset polynomial neural networks
dc.titleA subset polynomial neural networks approach for breast cancer diagnosis
dc.typeJournal article
local.bibliographicCitation.issue3
local.bibliographicCitation.lastpage302
local.bibliographicCitation.startpage293
local.contributor.affiliationO'Neill, Terence, College of Business and Economics, ANU
local.contributor.affiliationPenm, Jack HW, College of Business and Economics, ANU
local.contributor.affiliationPenm, Jonathan, College of Business and Economics, ANU
local.contributor.authoruidO'Neill, Terence, u7601382
local.contributor.authoruidPenm, Jack HW, u7800853
local.contributor.authoruidPenm, Jonathan, u4296024
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor150201 - Finance
local.identifier.ariespublicationu8902633xPUB42
local.identifier.citationvolume3
local.identifier.doi10.1504/IJEH.2007.014549
local.identifier.scopusID2-s2.0-34547814614
local.type.statusPublished Version

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