Association Rules for Diagnosis of Hiv-Aids  

Anubha Dubey
Department of Bioinformatics, MANIT, BHOPAL, India
Author    Correspondence author
Computational Molecular Biology, 2014, Vol. 4, No. 3   doi: 10.5376/cmb.2014.04.0003
Received: 04 Mar., 2014    Accepted: 15 Apr., 2014    Published: 01 May, 2014
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This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Anubha Dubey, 2014, Association Rules for Diagnosis of Hiv-Aids, Computational Molecular Biology, Vol.4, No.3 26-33 (doi: 10.5376/cmb.2014.04.0003)


Association rule mining is an active area of research in data mining. Data mining is a process of finding patterns from very large volumes of data. These patterns are important in making association rules and correlations among them. Recent years have witnessed many efforts on discovering associations for genes, proteins, enzymes, networks. In this study, association rules for HIV disease diagnosis is tried to generate. It describes the concept of different stages of HIV progression which are associated with other infections. As huge patient data is available, there is a need to develop some interesting patterns, associations, correlations for proper treatment and disease diagnosis. The efficiency and advantages of these rules has been used by medical practioners to diagnose the disease or recommend the suitable treatment.

Associations; Correlations; Pattern; HIV; Treatment
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