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

Authors:
Jesmin Nahar;Tasadduq Imam;Kevin S. Tickle;A. B. M. Shawkat Ali;Yi-Ping Phoebe Chen
Affiliations:
Central Queensland University, Faculty of Arts, Business, Informatics and Education, Queensland, Australia;Central Queensland University, Faculty of Arts, Business, Informatics and Education, Queensland, Australia;Central Queensland University, Faculty of Arts, Business, Informatics and Education, Queensland, Australia;Central Queensland University, Faculty of Arts, Business, Informatics and Education, Queensland, Australia;Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

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.