Artificial neural networks in cancer recurrence prediction

Panrasee Ritthipravat

doi:10.1109/ICCET.2009.84

Artificial neural networks in cancer recurrence prediction

Panrasee Ritthipravat

Department of Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

This paper aims to review the use of artificial neural networks (ANNs) in prediction of cancer recurrence. The sources of publications were randomly selected from PUBMED database, IEEE explore, and the google search engine with the keywords for searching as "recurrence" or "relapse" or "disease free" + "neural network" + "cancer". Increasing of the predictive performance was considered. In addition, handling incomplete data and feature selection techniques usually employed in this application were examined.

Original language	English
Title of host publication	Proceedings - 2009 International Conference on Computer Engineering and Technology, ICCET 2009
Pages	103-107
Number of pages	5
DOIs	https://doi.org/10.1109/ICCET.2009.84
Publication status	Published - 2009

Publication series

Name	Proceedings - 2009 International Conference on Computer Engineering and Technology, ICCET 2009
Volume	2

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Cancer recurrence
Feature selection
Incomplete data
Neural networks
Relapse

Access to Document

10.1109/ICCET.2009.84

Cite this

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title = "Artificial neural networks in cancer recurrence prediction",

abstract = "This paper aims to review the use of artificial neural networks (ANNs) in prediction of cancer recurrence. The sources of publications were randomly selected from PUBMED database, IEEE explore, and the google search engine with the keywords for searching as {"}recurrence{"} or {"}relapse{"} or {"}disease free{"} + {"}neural network{"} + {"}cancer{"}. Increasing of the predictive performance was considered. In addition, handling incomplete data and feature selection techniques usually employed in this application were examined.",

keywords = "Cancer recurrence, Feature selection, Incomplete data, Neural networks, Relapse",

author = "Panrasee Ritthipravat",

year = "2009",

doi = "10.1109/ICCET.2009.84",

language = "English",

isbn = "9780769535210",

series = "Proceedings - 2009 International Conference on Computer Engineering and Technology, ICCET 2009",

pages = "103--107",

booktitle = "Proceedings - 2009 International Conference on Computer Engineering and Technology, ICCET 2009",

}

Artificial neural networks in cancer recurrence prediction. / Ritthipravat, Panrasee.
Proceedings - 2009 International Conference on Computer Engineering and Technology, ICCET 2009. 2009. p. 103-107 4769567 (Proceedings - 2009 International Conference on Computer Engineering and Technology, ICCET 2009; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Artificial neural networks in cancer recurrence prediction

AU - Ritthipravat, Panrasee

PY - 2009

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N2 - This paper aims to review the use of artificial neural networks (ANNs) in prediction of cancer recurrence. The sources of publications were randomly selected from PUBMED database, IEEE explore, and the google search engine with the keywords for searching as "recurrence" or "relapse" or "disease free" + "neural network" + "cancer". Increasing of the predictive performance was considered. In addition, handling incomplete data and feature selection techniques usually employed in this application were examined.

AB - This paper aims to review the use of artificial neural networks (ANNs) in prediction of cancer recurrence. The sources of publications were randomly selected from PUBMED database, IEEE explore, and the google search engine with the keywords for searching as "recurrence" or "relapse" or "disease free" + "neural network" + "cancer". Increasing of the predictive performance was considered. In addition, handling incomplete data and feature selection techniques usually employed in this application were examined.

KW - Cancer recurrence

KW - Feature selection

KW - Incomplete data

KW - Neural networks

KW - Relapse

UR - https://www.scopus.com/pages/publications/65949111739

U2 - 10.1109/ICCET.2009.84

DO - 10.1109/ICCET.2009.84

M3 - Conference contribution

AN - SCOPUS:65949111739

SN - 9780769535210

T3 - Proceedings - 2009 International Conference on Computer Engineering and Technology, ICCET 2009

SP - 103

EP - 107

BT - Proceedings - 2009 International Conference on Computer Engineering and Technology, ICCET 2009

ER -

Artificial neural networks in cancer recurrence prediction

Abstract

Publication series

UN SDGs

Keywords

Access to Document

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