The effects produced due to CHD are constant fatigue,
physical disability, mental stress and depression. This paper
focuses on the creation of a data mining model using the
Random forest classification algorithm for evaluating and
predicting various events related to CHD. Some of these
studies, has made with the implementation of data mining
algorithm such as K-NN, Naïve bayes, K-means, ID3, and
Apriori algorithms. The growing healthcare burden and
suffering due to life threatening diseases such as heart disease
and the escalating cost of drug development can be
significantly reduced by design and development of novel
methods in data mining technologies and allied medical
informatics disciplines. In CHD, if the risk factors are
predicted in advance two sorts of problem can be solved.
First, various surgical treatments such as angioplasty,
coronary stents, coronary artery bypass and heart transplant
can be avoided to a great extent. Second, the associated cost
with each risk factor can be reduced.
The rest of the paper is organized as follows. Section II
discusses the literature review related to CHD. Section III
describes the material and methods used to develop the model.
Section IV concerns with the experimental results. Finally the
paper is concluded in Section V.