Analyse lifestyle related prostate cancer risk factors retrieved from literacy

Emmanuel Effiok
,
Enjie Liu
,
Jon Hitchcock

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Sustainable Development Goals

SDG 3 Good Health and Well

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.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 1136-1140

Publication milestones

Published - 01/02/2018

Publication status

Published - 01/02/2018

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781538630655

External Publication IDs

handle.net: 10547/623865
Scopus: 85047300579

Host publication title

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)