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Computational intelligence for microarray data and biomedical image analysis for the early diagnosis of breast cancer

journal contribution
posted on 06.12.2017, 00:00 by Jesmin Nahar, Tasadduq ImamTasadduq Imam, Kevin TickleKevin Tickle, A B M Shawkat AliA B M Shawkat Ali, YP Chen
The objective of this paper was to perform a comparative analysis of the computational intelligence algorithms to identify breast cancer in its early stages. Two types of data representations were considered: microarray based and medical imaging based. In contrast to previous researches, this research also considered the imbalanced nature of these data. It was observed that the SMO algorithm performed better for the majority of the test data, especially for microarray based data when accuracy was used as performance measure. Considering the imbalanced characteristic of the data, the Naive Bayes algorithm was seen to perform highly in terms of true positive rate (TPR). Regarding the influence of SMOTE, a well-known imbalanced data classification technique, it was observed that there was a notable performance improvement for J48, while the performance of SMO remained comparable for the majority of the datasets. Overall, the results indicated SMO as the most potential candidate for the microarray and image dataset considered in this research.

History

Volume

39

Issue

16

Start Page

12371

End Page

12377

Number of Pages

7

eISSN

1873-6793

ISSN

0957-4174

Location

United Kingdom

Publisher

Pergamon

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Arts, Business, Informatics and Education; La Trobe University; TBA Research Institute;

Era Eligible

Yes

Journal

Expert systems with applications.