A Diabetes is a life-threatening issue in
modern health care domain. With the use of data mining
techniques, diabetes factors and co morbid risk
conditions associated with diabetes has found. In order
to stifle the evolution of diabetes mellitus, applies
distributed association rule mining and summarization
techniques to electronic medical records. This helps to
discover set of risk factors and co morbid conditions in
distributed medical dataset using frequent item set
mining. In general, association rule mining (ARM)
generates bulky volume of data sets which need to
summarize certain rules over medical record. This
encompasses a novel approach to find the common
factors which lead to high risks of diabetes and co
morbid conditions associated with diabetes. This
performs both association rule mining and association
rule summarization techniques with improved
classification algorithms. Exiting systems aim to apply
association rule mining to electronic medical records to
discover sets of risk factors and their corresponding
subpopulations that represent patients at particularly
high risk of developing diabetes. Given the high
dimensionality of EMRs (Electronic Medical Records),
association rule mining generates a very large set of rules
which we need to summarize for easy clinical use. The
existing system reviewed four association rule set
summarization techniques and conducted a comparative
evaluation to provide guidance regarding the diabetes
risk prediction. In the field of medical domain, the
prediction of diabetes and its Co-Morbid in earlier stage
is important. We propose a set of methods to perform
the Co-Morbid prediction. The propose technique
named as SAM (Split and Merge), which is based on fast
distributed quantitative association rule mining and rule
filtering for prediction co morbid conditions associated
with diabetes. SAM algorithm is used to discover the
frequent data item sets and summarized data sets. In
performance comparison of proposed SAM with existing
BUS approach based on prediction efficiency SAM is
better than BUS.
Keywords:- Association Rule Mining(ARM), Diabetes, Co-
Morbid, Risk Prediction, Electronic Medical Record (EMR),
Split and Merge(SAM), Data Mining, Bottom-up
Summerization (BUS).