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Bio-Inspired Algorithms for Medical Data Analysis

Bio-Inspired Algorithms for Medical Data Analysis

Hanane Menad, Abdelmalek Amine
ISBN13: 9781522530046|ISBN10: 1522530045|EISBN13: 9781522530053
DOI: 10.4018/978-1-5225-3004-6.ch014
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MLA

Menad, Hanane, and Abdelmalek Amine. "Bio-Inspired Algorithms for Medical Data Analysis." Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management, edited by Reda Mohamed Hamou, IGI Global, 2018, pp. 251-275. https://doi.org/10.4018/978-1-5225-3004-6.ch014

APA

Menad, H. & Amine, A. (2018). Bio-Inspired Algorithms for Medical Data Analysis. In R. Hamou (Ed.), Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management (pp. 251-275). IGI Global. https://doi.org/10.4018/978-1-5225-3004-6.ch014

Chicago

Menad, Hanane, and Abdelmalek Amine. "Bio-Inspired Algorithms for Medical Data Analysis." In Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management, edited by Reda Mohamed Hamou, 251-275. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3004-6.ch014

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Abstract

Medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized for clinical diagnosis. Bio-inspired algorithms is a new field of research. Its main advantage is knitting together subfields related to the topics of connectionism, social behavior, and emergence. Briefly put, it is the use of computers to model living phenomena and simultaneously the study of life to improve the usage of computers. In this chapter, the authors present an application of four bio-inspired algorithms and meta heuristics for classification of seven different real medical data sets. Two of these algorithms are based on similarity calculation between training and test data while the other two are based on random generation of population to construct classification rules. The results showed a very good efficiency of bio-inspired algorithms for supervised classification of medical data.

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