Data mining plays an important role in the identification and
prediction of various sort of metabolic syndromes and hence
various sorts of diseases can be discovered. In the existing
work, Decision tree classification algorithm has been used to
assess the events related to CHD. The proposed work is
mainly concerned with the development of a data mining
model with the Random Forest classification algorithm. The
developed model will have the functionalities such as
predicting the occurrence of various events related to each
patient record, prevention of risk factors with its associated
cost metrics and an improvement in overall prediction
accuracy. As a result, the causes and the symptoms related to
each event will be made in accordance with the record related
to each patient and thereby CHD can be reduced to a great
extent.