University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach

Liu, S, Zeng, J, Gong, H, Yang, H, Zhai, J, Cao, Yi, Liu, J, Luo, Y, Li, Y, Maguire, L and Ding, X (2017) Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach Computers in Biology and Medicine, 92. pp. 168-175.

[img] Text
2017 - Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach.pdf - Accepted version Manuscript
Restricted to Repository staff only until 21 November 2018.

Download (1MB)

Abstract

Background

Breast cancer is the most prevalent cancer in women in most countries of the world. Many computer-aided diagnostic methods have been proposed, but there are few studies on quantitative discovery of probabilistic dependencies among breast cancer data features and identification of the contribution of each feature to breast cancer diagnosis.

Methods

This study aims to fill this void by utilizing a Bayesian network (BN) modelling approach. A K2 learning algorithm and statistical computation methods are used to construct BN structure and assess the obtained BN model. The data used in this study were collected from a clinical ultrasound dataset derived from a Chinese local hospital and a fine-needle aspiration cytology (FNAC) dataset from UCI machine learning repository.

Results

Our study suggested that, in terms of ultrasound data, cell shape is the most significant feature for breast cancer diagnosis, and the resistance index presents a strong probabilistic dependency on blood signals. With respect to FNAC data, bare nuclei are the most important discriminating feature of malignant and benign breast tumours, and uniformity of both cell size and cell shape are tightly interdependent.

Contributions

The BN modelling approach can support clinicians in making diagnostic decisions based on the significant features identified by the model, especially when some other features are missing for specific patients. The approach is also applicable to other healthcare data analytics and data modelling for disease diagnosis.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
NameEmailORCID
Liu, S
Zeng, J
Gong, H
Yang, H
Zhai, J
Cao, Yiyc0006@surrey.ac.uk
Liu, J
Luo, Y
Li, Y
Maguire, L
Ding, X
Date : 21 November 2017
Identification Number : 10.1016/j.compbiomed.2017.11.014
Copyright Disclaimer : © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords : Clinical decision support; Data modelling; Bayesian network; Quantitative analysis; Diagnostic contribution; Breast cancer diagnosis
Depositing User : Melanie Hughes
Date Deposited : 28 Nov 2017 12:53
Last Modified : 25 Jan 2018 14:11
URI: http://epubs.surrey.ac.uk/id/eprint/845054

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