Title

Fusion of FNA-cytology and Gene-expression Data Using Dempster-Shafer Theory of Evidence to Predict Breast Cancer Tumors

Authors

Mansoor Raza^1*, Iqbal Gondal^1,3, David Green¹, Ross L. Coppel^2,3

Affiliation

¹GSIT, Faculty of IT, ²Deparment of Microbiology, ³Victorian Bioinformatics Consortium, Monash University, Australia

E-mail*

Mansoor.Raza@Infotech.monash.edu.au; * Corresponding author

Article Type

Hypothesis

Date

received May 18, 2006; revised July11, 2006; accepted July 17, 2006; published online July 19, 2006

Abstract

Decision-in decision-out fusion architecture can be used to fuse the outputs of multiple classifiers from different diagnostic sources. In this paper, Dempster-Shafer Theory (DST) has been used to fuse classification results of breast cancer data from two different sources: gene-expression patterns in peripheral blood cells and Fine-Needle Aspirate Cytology (FNAc) data. Classification of individual sources is done by Support Vector Machine (SVM) with linear, polynomial and Radial Base Function (RBF) kernels. Out put belief of classifiers of both data sources are combined to arrive at one final decision. Dynamic uncertainty assessment is based on class differentiation of the breast cancer. Experimental results have shown that the new proposed breast cancer data fusion methodology have outperformed single classification models

Keywords

Data fusion; Dempster-Shafer Theory; Classification; SVM; Breast cancer; Microarray; FNAc

Citation

Raza et al., Bioinformation 1(5): 170-175 (2006)

Edited by

S. Krishnaswamy

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.