Abstract:
The healthcare system collects large
amounts of healthcare data which unfortunately,
are not mined to discover hidden information for
effective decision making. Discovery of hidden
patterns and relationships often goes unexploited.
Data mining techniques can help these conditions.
Nowadays, the diagnosis of diseases is a vital and
intricate job in medicine. Medical diagnosis is
regarded as an important yet complicated task
that needs to be executed accurately and
efficiently. An automatic medical diagnosis system
would probably be exceedingly beneficial by
bringing all of them together. This paper presents
a heart disease prediction models that can assist
professionals in predicting heart disease status
based on the clinical data of patients from UCI
data set. It analyzes of some data mining
algorithms such as Weighted Associative
Classifier (WAC), Naïve Bayesian and Decision
Tree Classifiers for making decision of the
conditions of heart disease. It enable significant
knowledge, e.g patterns, relationships between
medical factors related to heart disease. This
paper shows the prediction accuracy result of
three classifiers and which classifier are generate
more accurate result than other classifiers and
compares the generated accuracies by using
holdout method. Data mining enable the health
sector to predict patterns in the dataset.
