Identification of AIDS disease severity based on computational intelligence techniques using clonal selection algorithm. (2016)

• Record Type: Journal Article
• Authors: Devarapalli, Dharmaiah; Srikanth, Panigrahi; Rao, MandaRam Narasinga; Rao, J. Venkata
• Is Part Of: International journal of convergence computing. Volume 2: Number 3/4(2016)
• Page Start: 193
• Page End: 207
• Publication Date: 2016
• Subjects: AIDS data -- CD4 cells count -- fitness function -- intelligence technique -- clonal selection algorithm; Computer science -- Periodicals; 004.05
• Languages: English
• ISSNs: 2048-9129

Abstract

The mining bioinformatics data is a newly formed area in the process between bioinformatics and data mining. The process of developing the algorithms manipulated is based on computational intelligence. The agitation cases are created everywhere in the world concerning the Acquired Immunodeficiency Syndrome (AIDS) disease which are complex. Data were collected throughout self-administered mail survey nationwide at clinical and drug treatment centres and AIDS service organisations. Every country is facing the problem about AIDS occurring at a time immediately before the present survey according to the World Health Organization (WHO), especially that causing the death of many CD4 count cells. This paper identifies patients' severity and it is useful to doctors. This research identifies patients' severity and it is useful to doctors. In this paper in detail of machine learning algorithm of the clonal selection algorithm more effective diagnosis and optimise the data of AIDS disease. Those techniques achieved promising results.