The main objective of our paper is to learn the different
techniques of data mining used in prediction of heart disease
by using different data mining tools. Life is dependent on
efficient working of heart because heart is essential part of our
body. If operation of heart is not proper, it will affect the other
body parts of human such as brain, kidney etc. Heart disease is
a disease that affects on the operation of heart. There are
number of factors which increases risk of Heart disease.
Nowadays, in the world Heart disease is the major cause of
deaths. The World Health Organization (WHO) has estimated
that 12 million deaths occur worldwide, every year due to the
Heart diseases. In 2008, 17.3 million people died due to Heart
Disease. Over 80% of deaths in world are because of Heart
disease. WHO estimated by 2030, almost 23.6 million people
will die due to Heart disease as written in [10]. Prediction by
using data mining techniques gives us accurate result of
disease. IHDPS (intelligent heart disease prediction system)
can discover and extract hidden knowledge associated with
heart disease from a historical heart disease database. It can
answer complex queries for diagnosing heart disease and thus
help healthcare analysts and practitioners to make intelligent
clinical decisions which traditional decision support systems
cannot. In this paper analysis of various data mining techniques
given in tables which were used and helpful for medical
analysts or practitioners for accurate heart disease diagnosis.
1.1 The risk factor for heart disease
Family history of heart disease: - most people know that the
heat disease can run in families. That if anybody has a family
history of heart disease, he/she may be at greater risk for heart
attack, stroke and other heard diseases.
Smoking: - smoking is major cause of heart attack, stroke and
other peripheral arterial disease. Nearly 40% of all people who
die from smoking tobacco do so due of heart and blood vessel
diseases. A smoker’s risk of heart attack reduces rapidly after