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Identification of AIDS Disease Severity Using Genetic Algorithm

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSFOMEBI)

Abstract

Bioinformatics is a data intentionally the field in Research and Development. The purpose of bioinformatics data mining (DM) is to observe the relationships and patterns in large databases to provide useful data analysis and results. Evolutionary algorithms play a main role in computational intelligence techniques. An developing situation was created throughout the world regarding the human immunodeficiency virus infection/acquired immunodeficiency syndrome (HIV/AIDS) disease that is mainly stigma. Every country is facing this problem. According to present survey is World health organization (WHO), AIDS disease has its complexity are health disease going present century. A best way to early examine of AIDS may improve the lives of all people affected by AIDS and people may lead healthy life. In this part, we have present an evolutionary algorithm known as Genetic Algorithm (GA) for better results of AIDS disease using association rule mining. In this computational intelligence technique, we tested the performance of the method using AIDS dataset. We presented a better fitness function using coverage, comprehensibility, and rule length. This fitness function we achieved is promising accuracy for model.

Keywords

  • Artificial immune system
  • AIDS of CD4 cell count
  • Computational intelligence technique
  • Fitness function
  • Genetic algorithm

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Correspondence to Panigrahi Srikanth .

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Devarapalli, D., Srikanth, P. (2015). Identification of AIDS Disease Severity Using Genetic Algorithm. In: Muppalaneni, N., Gunjan, V. (eds) Computational Intelligence Techniques for Comparative Genomics. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-287-338-5_8

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  • DOI: https://doi.org/10.1007/978-981-287-338-5_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-337-8

  • Online ISBN: 978-981-287-338-5

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