Abstract:
Heart disease is the leading cause of death
in the world over the past 10 years. Researchers
have been using several data mining techniques to
help health care professionals in the diagnosis of
heart disease patients. Decision Tree is one of the
data mining techniques used in the diagnosis of
heart disease showing considerable success. Kmeans
clustering is one of the most popular
clustering techniques; however initial centroid
selection strongly affects its results.In this paper,
heart-disease dataset is considered for study.The
implemented system will be useful to find out the
patient’s level in the heart-diseases. This paper
implements integrating initial centroid selection of
the k-means clustering such as inlier and random
row methods with decision tree in the diagnosis of
heart disease patients. The result shows that
integrating k-means clustering with decision tree
with initial centroid selection could enhance the
accuracy in diagnosing heart disease patients. It
also shows that the inlier initial centroid selection
method could achieve higher accuracy than
random row initial centroid selection methods in
the diagnosis of heart disease patients.
