The document proposes applying data mining techniques to identify suitable heart disease treatments. It discusses using single and hybrid data mining on diagnosis and treatment data to determine if models can reliably predict treatments as they do diagnoses. The proposed system would apply various data mining algorithms to both diagnosis and treatment data to investigate if hybrid models improve treatment prediction accuracy over single techniques.

1. USING DATA MINING TECHNIQUES IN HEART DISEASE DIAGNOSIS
AND TREATMENT
OBJECTIVE:
The main Objective of the project Is by applying the predictive data
miningtechniques (both single and hybrid) to the heart disease patients for
diagnosing and also for the treatment of heart disease.
PROBLEM STATEMENT:
 Clinical decisions are often made based on doctors’ intuition and
experience rather than on the knowledge rich data hidden in the
database.
 Medical Misdiagnoses are a serious risk to our healthcare profession.
If they continue, then people will fear going to the hospital for
treatment. We can put an end to medical misdiagnosis by informing
the public and filing claims and suits against the medical practitioners
at fault.
ABSTRACT:
Disease diagnosis is one of the applications where data mining tools are
proving successful results. Heart disease is the leading cause of death all over the
world. Using Single Data Mining Technique in the diagnosis of heart disease has

2. been comprehensively investigated showing acceptable levels of accuracy.
However, using data mining techniques to identify a suitable treatment for heart
disease patients has received less attention. This paper identifies gaps in the
research on heart disease diagnosis and treatment and proposes a model to
systematically close those gaps to discover if applying data mining techniques to
heart disease treatment data can provide as reliable performance as that achieved in
diagnosing heart disease.
EXISTING SYSTEM:
 In Existing system, the single data mining technique is used to
diagnose the heart disease. There is no previous research that
identifies which data mining technique can provide more reliable
accuracy in identifying suitable treatment for heart disease patients.
 Practical use of healthcare database systems and knowledge discovery
is difficult in heart disease diagnosis.
DISADVANTAGES:
 Hospitals do not provide the same quality of service even though they
provide the same type of service.
 There is no previous research that identifies which data mining
technique can provide more reliable accuracy in identifying suitable
treatment for heart disease patients.
 It takes more time consumption for practical use of healthcare
database systems.

3. PROPOSED SYSTEM:
 In Proposed System, we are applying data mining techniques (Hybrid)
in identifying suitable treatments for heart disease patients.
 Apply single data mining techniques to heart disease diagnosis
benchmark dataset to establish baseline accuracy for each single data
mining technique in the diagnosis of heart disease patients.
 Apply the same single data mining techniques used in heart disease
diagnosis to heart disease treatment dataset to investigate if single data
mining techniques can achieve equivalent (or better) results in
identifying suitable treatments as that achieved in the diagnosis.
 Apply hybrid data mining techniques to heart disease diagnosis
benchmark dataset to establish baseline accuracy for each hybrid data
mining technique in the diagnosis of heart disease patients.
 Apply the same hybrid data mining techniques used in heart disease
diagnosis to heart disease treatment dataset to investigate if hybrid
data mining techniques can achieve equivalent (or better) results in
identifying suitable treatments as that achieved in the diagnosis.
ADVANTAGES:
 By applying data mining techniques to help health care professionals
in the diagnosis of heart disease.

4.  Hybrid data mining techniques are used for selecting the suitable
treatment for heart disease patients.
 Time consumption is less.
 High Performance and Accuracy.
ALGORITHM USED:
1. Navie Bayes
2. Decision Tree
3. Neural Networks
4. Association Rule
5. Regression
ARCHITECTURE DIAGRAM:

5. SYSTEM REQUIREMENTS:
Hardware Requirements:
• Intel Pentium IV
• 256/512 MB RAM
• 1 GB Free disk space or greater
• 1 GB on Boot Drive
• 17” XVGA display monitor
• 1 Network Interface Card (NIC)
Software Requirements:
• MS Windows XP/ Windows 7
• MS IE Browser 6.0/later
• MS Dot Net Framework 4.0
• MS Visual Studio.Net 2010
• Internet Information Server (IIS)
• MS SQL Server 2005
• Windows Installer 3.1
Applications
 Hospitals Management
 Health care Institutions.