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Association Rules for Diagnosis of Hiv-Aids
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the generation of a large number of rules, most of
which are uninteresting to the user. Instead, it is
augmented the support-confidence framework with a
correlation measure, resulting in the mining of
correlation rules. The added measure substantially
reduces the number of rules generated, and leads to
the discovery of more meaningful rules. However,
there seems to be no single correlation measure that
works well for all cases. Unfortunately, most such
measures do not have the null invariance property.
Because large data sets typically have many
null-transactions, it is important to consider the
null-invariance property when selecting appropriate
interestingness measures in the correlation analysis.
Our analysis shows that both all confidence and
cosine are good correlation measures for large
applications, although it is wise to augment them with
additional tests, such as lift, when the test result is not
conclusive. There are other correlations measures are
also given in literature that can be used for analysis.
4 Conclusion and Future work
The improvement of new technologies raises data
collection and accumulation. Without appropriate
processing and interpretation this information is
useless. There are four standard methods of data
mining:
Association,
classification,
clustering
techniques and prediction. For most medical
applications, the logical rules are not precise but
vague and the uncertainty is present both in premise
and decision. For this kind of application a good
methodology is the rule representation from
decision-tree method, which is easily understood by
user. Therefore the integration of fuzzy set and data
mining methods gives a much better and more exact
representation of relationship between symptoms and
diagnosis. These associative patterns are useful in
classification on the basis of certain properties.
Different association’s patterns can be found by the
rules discussed and further correlation can be studied
by these patterns. These patterns can be useful for
medical diagnostics to improve the condition of
patients by proper medications. Further associated
diseases can be finding by associated symptoms. Like
HIV-tuberculosis other associated pattern of disease
can be diagnosed and treated simultaneously. These
analyses are useful for biomedical scientists to further
improve the formulations of drugs for treatment of
diseases.
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