Cholesterol: - abnormal levels of lipids (fats) in the blood are
risk factor of heart diseases. Cholesterol is a soft, waxy
substance found among the lipids in the bloodstream and in all
the body’s cells. High level of triglyceride (most common type
of fat in body) combined with high levels of LDL (low density
lipoprotein) cholesterol speed up atherosclerosis increasing the
risk of heart diseases.
High blood pressure: - High blood pressure also known as
HBP or hypertension is a widely misunderstood medical
condition. High blood pressure increase the risk of the walls of
our blood vessels walls becoming overstretched and injured.
Also increase the risk of having heart attack or stroke and of
developing heart failure, kidney failure and peripheral vascular
disease.
Obesity:-the term obesity is used to describe the health
condition of anyone significantly above his or her ideal healthy
weight. Being obese puts anybody at a higher risk for health
problem such as heart disease, stroke, high blood pressure,
diabetes and more.
Lack of physical exercise: -lack of exercise is a risk factor for
developing coronary artery disease (CAD). Lack of physical
exercise increases the risk of CAD, because it also increases the
risk for diabetes and high blood pressure.
2. Literature Survey
Heart disease is a term that assigns to a large number of
medical conditions related to heart. These medical conditions
describe the abnormal health conditions that directly influence
the heart and all its parts. Heart disease is a major health
problem in today’s time. This paper aims at analyzing the
various data mining techniques introduced in recent years for
heart disease prediction. Table 1 shows different data mining
techniques used in the diagnosis of Heart disease over different
Heart disease datasets. In some papers this is given that they
use only one technique for diagnosis of heart disease as given
in Shadab et al [12], Carlos et al [ 5] etc. but in case of other
research work more than one data mining techniques are used