Diagnosing prostate cancer using backpropagation neural network and greedy decision procedure

Published date
2010-05
Resource type
Publisher
ISBN
ISSN
DOI
Call no.
Other identifier(s)
Edition
Copyrighted date
Language
eng
File type
application/pdf
Extent
6 pages
Other title(s)
Advisor
Other Contributor(s)
Citation
Proceedings of the 7th International Joint Conference on Computer Science and Software Engineering (JCSSE 2010). Ramkhamhaeng University, Bangkok, Thailand. (May 12-14, 2010), 43-48
Degree name
Degree level
Degree discipline
Degree department
Degree grantor
Abstract
A novel procedure for diagnosing prostate cancer (PC) based on Back propagation Neural Network (BPNN) is proposed. Elderly men with symptoms such as urinary retention, urinary hesitancy, urinary dribbling, burning urination, hematuria, etc. are considered as primary attributes. Prostate-specific antigen (PSA) level and Gleason score are the secondary attributes. Initial dataset is generated based on the clinical database. The BPNN assigns symptom levels of a set of patients based on their primary attributes. A greedy decision procedure predicts tumor stages of patients based on their strong symptom levels and secondary attributes. The simulation shows that the proposed procedure is an effective way for diagnosing prostate tumor stages.
Table of contents
Description
punsarn.dc.description.sponsorship
Spatial Coverage
Subject(s)
Rights
Access rights
Rights holder(s)
Location
View External Resources