Use of neural network analysis to diagnose breast cancer patients

Raymond Fang, Vincent To Yee Ng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

In diagnosis of breast cancer, several different diagnostic tests can be conducted on same patient simultaneously so as to improve the diagnostic results. There are three tumor makers currently available for breast cancer diagnosis, namely CEA, CA15.3, and MCA. The purpose of this study was to investigate the usefulness of neural networks to distinguish breast cancer patients from normal people based on the pattern of the three tumor maker measurements. The neural networks was built and trained with the training data set, and then tested with a separate data set. In order to evaluate the performance of the neural network which differentiated breast carcinoma from normal conditions, an advanced statistical method, relative operating characteristic (ROC) analysis, was utilized.
Original languageEnglish
Title of host publicationProc 1993 IEEE Reg 10 Conf Comput Commun Control Power Eng (TENCON '93)
PublisherPubl by IEEE
Pages841-844
Number of pages4
ISBN (Print)0780312333
Publication statusPublished - 1 Dec 1993
Externally publishedYes
EventProceedings of the 1993 IEEE Region 10 Conference on Computer, Communication, Control aand Power Engineering. Part 3 (of 5) - Beijing, China
Duration: 19 Oct 199321 Oct 1993

Conference

ConferenceProceedings of the 1993 IEEE Region 10 Conference on Computer, Communication, Control aand Power Engineering. Part 3 (of 5)
CountryChina
CityBeijing
Period19/10/9321/10/93

ASJC Scopus subject areas

  • Engineering(all)

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