Cancer recognition from DNA microarray gene expression data using averaged one-dependence estimators

Shoon , Lei Win and Htike@Muhammad Yusof, Zaw Zaw and Yusof, Faridah and Ibrahim Ali , Noorbatcha (2014) Cancer recognition from DNA microarray gene expression data using averaged one-dependence estimators. International Journal on Cybernetics & Informatics (IJCI) , 3 (2). pp. 1-10. ISSN 2277-548X (O) 2320-8430 (P)

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Abstract

Cancer is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very late. Therefore, it is of paramount importance to prevent and detect cancer early. Nonetheless, conventional methods of detecting and diagnosing cancer rely solely on skilled physicians, with the help of medical imaging, to detect certain symptoms that usually appear in the late stages of cancer. The microarray gene expression technology is a promising technology that can detect cancerous cells in early stages of cancer by analyzing gene expression of tissue samples. The microarray technology allows researchers to examine the expression of thousands of genes simultaneously. This paper describes a state-of-the-art machine learning based approach called averaged one-dependence estimators with subsumption resolution to tackle the problem of recognizing cancer from DNA microarray gene expression data. To lower the computational complexity and to increase the generalization capability of the system, we employ an entropy-based gene selection approach to select relevant gene that are directly responsible for cancer discrimination. This proposed system has achieved an average accuracy of 98.94% in recognizing and classifying cancer over 11 benchmark cancer datasets. The experimental results demonstrate the efficacy of our framework.

Item Type:	Article (Journal)
Additional Information:	6919/37774
Uncontrolled Keywords:	Cancerrecognition; microarray gene expression; AODEsr
Subjects:	Q Science > Q Science (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering
Depositing User:	Mr. Zaw Zaw Htike
Date Deposited:	19 Aug 2014 08:33
Last Modified:	19 Jun 2018 16:27
URI:	http://irep.iium.edu.my/id/eprint/37774

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