Diagnosing prostate cancer using backpropagation neural network and greedy decision procedure
Diagnosing prostate cancer using backpropagation neural network and greedy decision procedure
dc.contributor.author | Gopalakrishnan, Anilkumar Kothalil | |
dc.contributor.author | Thitipong Tanprasert | |
dc.contributor.author | Faculty of Science and Technology | |
dc.date.accessioned | 2016-06-15T01:37:13Z | |
dc.date.available | 2016-06-15T01:37:13Z | |
dc.date.issued | 2010-05 | |
dc.description.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. | en_US |
dc.format.extent | 6 pages | en_US |
dc.format.mimetype | application/pdf | en_US |
dc.identifier.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 | en_US |
dc.identifier.uri | https://repository.au.edu/handle/6623004553/17943 | |
dc.language.iso | eng | en_US |
dc.subject | Backpropagation neural network | en_US |
dc.subject | Prostate cancer | en_US |
dc.subject | Prostate-specific antigen | en_US |
dc.subject | Gleason score | en_US |
dc.subject | Symptom level | en_US |
dc.subject | Greedy decision procedure | en_US |
dc.title | Diagnosing prostate cancer using backpropagation neural network and greedy decision procedure | en_US |
dc.type | Text | en_US |
mods.genre | Proceeding Paper | en_US |
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