Abstract:
Data mining is the process of discovering
interesting knowledge, such as patterns,
associations, changes, anomalies and significant
structures, from large amounts of data stored in
databases, data warehouses, or other information
repositories. In this paper, we have proposed an
efficient approach for the extraction of significant
patterns from the patients database for diabetes
prediction. The diagnosis of diseases is a significant
and tedious task in medicine. To facilitate the
diagnosis process, the effort to utilize knowledge and
experience of numerous specialists and clinical
screening data of patients collected in databases is
considered a valuable option. The patients database
is clustered using the KMIX clustering algorithm,
which will extract the data relevant to diabetes from
the database. Subsequently the frequent patterns are
mined from the extracted data, relevant to diabetes,
using the MAFIA algorithm. Then the significant
patterns to diabetes diagnosis are chosen from these
frequent patterns. These patterns can be used to
apply in the healthcare system.
