| dc.description.abstract | Data mining plays an efficient role in prediction
of diseases in health care industry. Diabetes has become
one of the major global health problems at present.
According to the WHO 2014 report, around 422 million
people worldwide are suffering from diabetes. Diabetes is a
metabolic disease where the improper management of
blood glucose levels led to risk of many diseases like heart
attack, kidney disease, eye etc. Many algorithms have been
developed for the prediction of diabetes and accuracy
estimation. This paper gives detailed evaluation of existing
data mining methods used for prediction of diabetes.
More than twenty research papers had been referred
during this research work. And according to those research
papers, decision tree related algorithms had been used in
most of the research works and this algorithm has given the
best performance also. So that, to have the best accuracy
and performance in data mining related projects, decision
tree algorithm and related algorithms can be used.
Further, it gives some idea about the tools which can be
used in data mining. WEKA, Orange, MATLAB, Tanagra and
Rapid Miner are some of the data mining tools which are
commonly used. Some of the researchers have used more
than one tool for their research works. But almost all of the
referred papers have used the data mining tool WEKA.
Finally, this paper gives the idea that, using Decision tree
related algorithms may give high performance and high
accuracy in data mining related works. | |
