University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Bayesian network modelling on data from fine needle aspiration cytology examination for breast cancer diagnosis

Ding, Xuemei, Cao, Yi, Zhai, Jia, Maguire, Liam, Li, Yuhua, Yang, Hongqin, Wang, Yuhua, Zeng, Jinshu and Liu, Shuo (2017) Bayesian network modelling on data from fine needle aspiration cytology examination for breast cancer diagnosis In: 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017), 24-25 Jun 2017, Taiyuan, China.

[img]
Preview
Text
Bayesian network modelling on data from fine needle aspiration cytology examination for breast cancer diagnosis.pdf - Version of Record
Available under License Creative Commons Attribution.

Download (1MB) | Preview

Abstract

The paper employed Bayesian network (BN) modelling approach to discover causal dependencies among different data features of Breast Cancer Wisconsin Dataset (BCWD) derived from openly sourced UCI repository. K2 learning algorithm and k-fold cross validation were used to construct and optimize BN structure. Compared to Na‹ve Bayes (NB), the obtained BN presented better performance for breast cancer diagnosis based on fine needle aspiration cytology (FNAC) examination. It also showed that, among the available features, bare nuclei most strongly influences diagnosis due to the highest strength of the influence (0.806), followed by uniformity of cell size, then normal nucleoli. The discovered causal dependencies among data features could provide clinicians to make an accurate decision for breast cancer diagnosis, especially when some features might be missing for specific patients. The approach can be potentially applied to other disease diagnosis.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
NameEmailORCID
Ding, Xuemei
Cao, Yiyc0006@surrey.ac.uk
Zhai, Jia
Maguire, Liam
Li, Yuhua
Yang, Hongqin
Wang, Yuhua
Zeng, Jinshu
Liu, Shuo
Date : May 2017
Identification Number : 10.2991/fmsmt-17.2017.86
Copyright Disclaimer : © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/
Uncontrolled Keywords : Bayesian networks; Data modelling; Quantitative analysis; Breast cancer diagnosis
Additional Information : Hard copy printed publish-on-demand by Curran Associates, Inc. ISBN: 9781510840232. Series title: Advances in Engineering Research, Volume 130
Depositing User : Clive Harris
Date Deposited : 15 Dec 2017 14:57
Last Modified : 15 Dec 2017 14:57
URI: http://epubs.surrey.ac.uk/id/eprint/845429

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800