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

DOI

10.2991/fmsmt-17.2017.86How to use a DOI?

Keywords

Bayesian networks, Data modelling, Quantitative analysis, Breast cancer diagnosis

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shuo Liu
AU  - Jinshu Zeng
AU  - Yuhua Wang
AU  - Hongqin Yang
AU  - Yuhua Li
AU  - Liam Maguire
AU  - Jia Zhai
AU  - Yi Cao
AU  - Xuemei Ding
PY  - 2017/04
DA  - 2017/04
TI  - Bayesian network modelling on data from fine needle aspiration cytology examination for breast cancer diagnosis
BT  - Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
PB  - Atlantis Press
SP  - 409
EP  - 412
SN  - 2352-5401
UR  - https://doi.org/10.2991/fmsmt-17.2017.86
DO  - 10.2991/fmsmt-17.2017.86
ID  - Liu2017/04
ER  -