IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Bio-Inspired Algorithms for Medical Data Analysis

Bio-Inspired Algorithms for Medical Data Analysis
View Sample PDF
Author(s): Hanane Menad (Dr. Tahar Moulay University Saida, Algeria) and Abdelmalek Amine (GeCoDe Laboratory, Department of Computer Sciences, Dr. Tahar Moulay University of Saida, Algeria)
Copyright: 2018
Pages: 25
Source title: Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management
Source Author(s)/Editor(s): Reda Mohamed Hamou (Dr. Tahar Moulay University of Saida, Algeria)
DOI: 10.4018/978-1-5225-3004-6.ch014

Purchase

View Bio-Inspired Algorithms for Medical Data Analysis on the publisher's website for pricing and purchasing information.

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.

Related Content

Tlou Maggie Masenya, Collence Takaingenhamo Chisita. © 2022. 21 pages.
Mmaphuti Carron Teffo, Ignitia Motjolopane, Tlou Maggie Masenya. © 2022. 18 pages.
Neha Lata, Valentine Joseph Owan. © 2022. 17 pages.
Rexwhite Tega Enakrire, Joseph Kehinde Fasae. © 2022. 13 pages.
Madireng Monyela. © 2022. 18 pages.
Valentine Joseph Owan, Daniel Clement Agurokpon. © 2022. 16 pages.
Nkholedzeni Sidney Netshakhuma. © 2022. 18 pages.
Body Bottom