Heart pattern discovery, pattern evaluation and knowledge presentation.

Heart disease is
the class of diseases that involve the heart or blood vessels. It is one of the most-flying
diseases of the modern world. The diagnosis of the heart disease should be accurately and correctly. Normally
it is diagnosed by using a medical specialist. If we use the techniques
integrated with the medical information system then it would be more advantageous
and it will reduce the cost also. This can be done after comparing different
data mining techniques for finding their suitability. Data mining combines
statistical analysis, machine learning algorithms and database technology to
extract hidden patterns and relationships from large databases. The
diagnosis of heart disease depends on clinical and pathological data. Heart
disease prediction system can assist medical professionals in predicting heart
disease status based on the clinical data of patients.Researchers have been applying different data
mining techniques to help health care professionals with improved accuracy in
the diagnosis of heart disease. Neural network, Naïve Bayes, Genetic algorithm,
Decision Tree, classification via clustering, and direct kernel self-organizing
map are some techniques used. By using some data mining techniques heart
disease prediction can be made simple by various characteristic to find out
whether the person suffers from heart attack or not, and it also takes less
time to for the prediction and improve the medical diagnosis of diseases with
good accuracy and minimizes the occurrence of heart attack. Data mining along
with soft computing techniques helps to unravel hidden relationships and
diagnose diseases efficiently even with uncertainties and inaccuracies.

Data Mining is about explaining the past and
predicting the future by means of data analysis. It is a field which combines
statistics, machine learning, artificial intelligence and database technology.
The value of data mining applications is often estimated to be very high.
Disease
prediction plays most important role in the data mining. Data Mining is a
process of discovering interesting patterns and knowledge from huge amount of
data. It refers to extracting or mining knowledge from large amount of data.
Extracting knowledge it is also called knowledge mining from data or knowledge
extraction or Knowledge Discovery from Data (KDD). The knowledge discovery
process typically involves data cleaning, data integration, data selection,
data transformation, pattern discovery, pattern evaluation and knowledge
presentation. Nowadays, healthcare organization generates a voluminous data
that results lack of information to make the right decision. Data mining
techniques can be used to extract the needful information from healthcare
organizations.