Computational intelligence for microarray data and biomedical image analysis for the early diagnosis of breast cancer |
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| Authors: | Jesmin Nahar Tasadduq Imam Kevin S Tickle ABM Shawkat Ali Yi-Ping Phoebe Chen |
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| Affiliation: | 1. Central Queensland University, Faculty of Arts, Business, Informatics and Education, Queensland, Australia;2. Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086, Australia;1. Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers–Robert Wood Johnson Medical School, New Brunswick, New Jersey;2. Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois;3. Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin;4. Department of Radiation Oncology, University of California-Davis, Sacramento, California;6. Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania;5. Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan;7. Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;11. Division of Imaging Science, Department of Radiology, Ohio State University, Columbus, Ohio;12. Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri;8. Department of Radiation Oncology, St. Anthony''s Cancer Center, St. Louis, Missouri;10. Radiation Research Program, National Cancer Institute, Bethesda, Maryland;9. Department of Radiation Oncology, University of California-San Francisco, San Francisco, California;71. Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio;1. Department of Computer Science, Universidade de A Coruña, 15071 A Coruña, Spain;2. Department of Computer Science and Artificial Intelligence, Universidad de Granada, 18071 Granada, Spain;3. Faculty of Computing and Information Technology – North Jeddah, King Abdulaziz University, 21589 Jeddah, Saudi Arabia;1. Life Sciences, Centrum Wiskunde & Informatica, Amsterdam, the Netherlands;2. Mathematical Institute, Leiden University, Leiden, the Netherlands |
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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. |
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