Analyse lifestyle related prostate cancer risk factors retrieved from literacy

Emmanuel Effiok; Enjie Liu; Jon Hitchcock

doi:10.1109/iThings-GreenCom-CPSCom-SmartData.2017.174

Analyse lifestyle related prostate cancer risk factors retrieved from literacy

Emmanuel Effiok
, Enjie Liu
, Jon Hitchcock

Institute for Research in Applicable Computing (IRAC)

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

1 Citation (Scopus)

Abstract

Risk factors for prostate cancer were identified through extensive research of literature and data was retrieved from both literatures and repositories. The research applies data mining techniques to the medical literatures and evidences on prostate cancer, with the aim to unravel the relationships between the presence of having multiple lifestyle factors and prostate cancer effective of occurrence of multiple factors. The research is to establish a possible predictive model based on theorized and proven risk factors and associations used in prostate cancer research. This paper describes the use of data mining algorithms on the risk factors to identify hidden knowledge. Firstly, an association rule mining algorithm is employed to identify the significant risk factors for the predictive modeling, based on the support level in terms of research materials used and confidence values. Secondly, the chosen factors were combined, modelled and visually represented to show their probability risks in relation to each other and the disease.

Original language	English
Title of host publication	nan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1136-1140
ISBN (Print)	9781538630655
DOIs	https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.174
Publication status	Published - 1 Feb 2018
Event	2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) - Exeter Duration: 21 Jun 2017 → 23 Jun 2017

Conference

Conference	2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
City	Exeter
Period	21/06/17 → 23/06/17
Other	2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (21/06/2017-23/06/2017, Exeter)

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

apriori algorithm
association rule mining
knowledge extraction
risk factor

Access to Document

10.1109/iThings-GreenCom-CPSCom-SmartData.2017.174

https://ieeexplore.ieee.org/document/8276897

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Cite this

@inproceedings{c652545543734db99ea0a49c58a54646,

title = "Analyse lifestyle related prostate cancer risk factors retrieved from literacy",

abstract = "Risk factors for prostate cancer were identified through extensive research of literature and data was retrieved from both literatures and repositories. The research applies data mining techniques to the medical literatures and evidences on prostate cancer, with the aim to unravel the relationships between the presence of having multiple lifestyle factors and prostate cancer effective of occurrence of multiple factors. The research is to establish a possible predictive model based on theorized and proven risk factors and associations used in prostate cancer research. This paper describes the use of data mining algorithms on the risk factors to identify hidden knowledge. Firstly, an association rule mining algorithm is employed to identify the significant risk factors for the predictive modeling, based on the support level in terms of research materials used and confidence values. Secondly, the chosen factors were combined, modelled and visually represented to show their probability risks in relation to each other and the disease.",

keywords = "apriori algorithm, association rule mining, knowledge extraction, risk factor",

author = "Emmanuel Effiok and Enjie Liu and Jon Hitchcock",

year = "2018",

month = feb,

day = "1",

doi = "10.1109/iThings-GreenCom-CPSCom-SmartData.2017.174",

language = "English",

isbn = "9781538630655",

pages = "1136--1140",

booktitle = "nan",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) ; Conference date: 21-06-2017 Through 23-06-2017",

}

Effiok, E, Liu, E & Hitchcock, J 2018, Analyse lifestyle related prostate cancer risk factors retrieved from literacy. in nan. Institute of Electrical and Electronics Engineers Inc., pp. 1136-1140, 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, 21/06/17. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.174

TY - GEN

T1 - Analyse lifestyle related prostate cancer risk factors retrieved from literacy

AU - Effiok, Emmanuel

AU - Liu, Enjie

AU - Hitchcock, Jon

PY - 2018/2/1

Y1 - 2018/2/1

N2 - Risk factors for prostate cancer were identified through extensive research of literature and data was retrieved from both literatures and repositories. The research applies data mining techniques to the medical literatures and evidences on prostate cancer, with the aim to unravel the relationships between the presence of having multiple lifestyle factors and prostate cancer effective of occurrence of multiple factors. The research is to establish a possible predictive model based on theorized and proven risk factors and associations used in prostate cancer research. This paper describes the use of data mining algorithms on the risk factors to identify hidden knowledge. Firstly, an association rule mining algorithm is employed to identify the significant risk factors for the predictive modeling, based on the support level in terms of research materials used and confidence values. Secondly, the chosen factors were combined, modelled and visually represented to show their probability risks in relation to each other and the disease.

AB - Risk factors for prostate cancer were identified through extensive research of literature and data was retrieved from both literatures and repositories. The research applies data mining techniques to the medical literatures and evidences on prostate cancer, with the aim to unravel the relationships between the presence of having multiple lifestyle factors and prostate cancer effective of occurrence of multiple factors. The research is to establish a possible predictive model based on theorized and proven risk factors and associations used in prostate cancer research. This paper describes the use of data mining algorithms on the risk factors to identify hidden knowledge. Firstly, an association rule mining algorithm is employed to identify the significant risk factors for the predictive modeling, based on the support level in terms of research materials used and confidence values. Secondly, the chosen factors were combined, modelled and visually represented to show their probability risks in relation to each other and the disease.

KW - apriori algorithm

KW - association rule mining

KW - knowledge extraction

KW - risk factor

U2 - 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.174

DO - 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.174

M3 - Conference contribution

SN - 9781538630655

SP - 1136

EP - 1140

BT - nan

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)

Y2 - 21 June 2017 through 23 June 2017

ER -

Analyse lifestyle related prostate cancer risk factors retrieved from literacy

Abstract

Conference

UN SDGs

Keywords

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