ABSTRACT
The successful application of data mining in highly visible fields
like e-business, marketing and retail has led to its application in
other industries and sectors. Among these sectors just discovering
is healthcare. The healthcare environment is still „information
rich‟ but „knowledge poor‟. There is a wealth of data available
within the healthcare systems. However, there is a lack of
effective analysis tools to discover hidden relationships and trends
in data. This research paper intends to provide a survey of current
techniques of knowledge discovery in databases using data mining
techniques that are in use in today‟s medical research particularly
in Heart Disease Prediction. Number of experiment has been
conducted to compare the performance of predictive data mining
technique on the same dataset and the outcome reveals that
Decision Tree outperforms and some time Bayesian
classification is having similar accuracy as of decision tree but
other predictive methods like KNN, Neural Networks,
Classification based on clustering are not performing well. The
second conclusion is that the accuracy of the Decision Tree and
Bayesian Classification further improves after applying genetic
algorithm to reduce the actual data size to get the optimal subset of
attribute sufficient for heart disease prediction.