Bayesian network modelling on data from fine needle aspiration cytology examination for breast cancer diagnosis
Shuo Liu, Jinshu Zeng, Yuhua Wang, Hongqin Yang, Yuhua Li, Liam Maguire, Jia Zhai, Yi Cao, Xuemei Ding
Available Online April 2017.
- https://doi.org/10.2991/fmsmt-17.2017.86How to use a DOI?
- Bayesian networks, Data modelling, Quantitative analysis, Breast cancer diagnosis
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/fmsmt-17.2017.86 ID - Liu2017/04 ER -