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Cancer Classification From DNA Microarray Using Genetic Algorithms and Case-Based Reasoning

Cancer Classification From DNA Microarray Using Genetic Algorithms and Case-Based Reasoning

Lilybert Machacha, Prabir Bhattacharya
Copyright: © 2021 |Volume: 13 |Issue: 1 |Pages: 21
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781799860648|DOI: 10.4018/IJSSCI.2021010102
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MLA

Machacha, Lilybert, and Prabir Bhattacharya. "Cancer Classification From DNA Microarray Using Genetic Algorithms and Case-Based Reasoning." IJSSCI vol.13, no.1 2021: pp.17-37. http://doi.org/10.4018/IJSSCI.2021010102

APA

Machacha, L. & Bhattacharya, P. (2021). Cancer Classification From DNA Microarray Using Genetic Algorithms and Case-Based Reasoning. International Journal of Software Science and Computational Intelligence (IJSSCI), 13(1), 17-37. http://doi.org/10.4018/IJSSCI.2021010102

Chicago

Machacha, Lilybert, and Prabir Bhattacharya. "Cancer Classification From DNA Microarray Using Genetic Algorithms and Case-Based Reasoning," International Journal of Software Science and Computational Intelligence (IJSSCI) 13, no.1: 17-37. http://doi.org/10.4018/IJSSCI.2021010102

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Abstract

There are many similarities in the symptoms of several types of cancer and that makes it sometimes difficult for the physicians to do an accurate diagnosis. In addition, it is a technical challenge to classify accurately the cancer cells in order to differentiate one type of cancer from another. The DNA microarray technique (also called the DNA chip) has been used in the past for the classification of cancer but it generates a large volume of noisy data that has many features, and is difficult to analyze directly. This paper proposes a new method, combining the genetic algorithm, case-based reasoning, and the k-nearest neighbor classifier, which improves the performance of the classification considerably. The authors have also used the well-known Mahalanobis distance of multivariate statistics as a similarity measure that improves the accuracy. A case-based classifier approach together with the genetic algorithm has never been applied before for the classification of cancer, same with the application of the Mahalanobis distance. Thus, the proposed approach is a novel method for the cancer classification. Furthermore, the results from the proposed method show considerably better performance than other algorithms. Experiments were done on several benchmark datasets such as the leukemia dataset, the lymphoma dataset, ovarian cancer dataset, and breast cancer dataset.

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