1. MULTI DISEASE CLASSIFICATION BASED ON EFFECTIVE ANALYTICAL TECHNIQUES Guide: Mr.R. Nandhi kesavan S.Aabitha Banu A.Karthika

2. SYNOPSIS: Objective Abstract Introduction Existing system Disadvantages Proposed system Advantages Performance metrics Result Conclusion and future work References

3. OBJECTIVE: The Objective of our project is to diagnosing Type 2 Diabetes and also provide treatment suggestion for that.

4. ABSTRACT: Data mining tools are proving successful results. Using Single Data Mining Technique showing acceptable levels of accuracy. Body fat distribution and obesity are important risk factors for type 2 diabetes. This study aims to predict the fasting plasma glucose (FPG) status.

5. INTRODUCTION: INTRODUCTION: Data mining is to extract hidden and previously unknown patterns. The relationships and knowledge that are difficult to detect with traditional statistical methods. Data mining applications in healthcare include prevention of hospital errors, early detection, prevention of diseases.

6. EXISTING SYSTEM : In Existing system, the single data mining technique is used to diagnose the disease. There is no previous research that identifies which data mining technique can provide more reliable accuracy. They uses only the internal measures to measure the fasting plasma glucose for predicting the type2 diabetes.

7. 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. It takes more time.

8. PROPOSED SYSTEM: In Proposed System, we are applying hybrid data mining techniques in identifying suitable treatments suggestion for Type 2 diabetes patients. We are using the symptoms of patients for diagnosing the type2 diabetes and also provide a treatment suggestion also.

9. MODULES: Analysing the Dataset Algorithm Implementation Pattern Formation Designing the Questionnaire Mining the Data set Diagnosing the Disease Treatment Suggestion

10. ADVANTAGES: Time consumption is less. High Performance and Accuracy. Data mining techniques is helpful for health care professionals in the type2 diabetes disease.

11. ARCHITECTURE DIAGRAM: Single data mining techniques Hybrid data mining techniques Type2 diabetes Provide accuracy result probability HIV prediction Cancer prediction Predefined data set Input attribute

12. PERFORMANCE METRICS: In our project we use the symptoms of the people for diagnosing the type2 diabetes. We create queries that contains the symptoms of type2 diabetes. The user can login our website to diagnosis he/she having type2 diabetes or not.

13. DATABASE DESIGN:

14. SCREEN SHOTS:

16. CONCLUSION AND FUTURE ENHANCEMENT: If patients or individuals were diagnosed as at risk for diabetes by the models, they may reduce the risk by individual strategies for a healthier lifestyle, such as healthy eating habits and exercises. The future enhancement is to use only which data mining techniques provide accurate results.

17. REFERENCES: 1. Han, j. and M. Kamber, Data Mining Concepts and Techniques. 2006: Morgan Kaufmann Publishers. 2. Lee, I.-N., S.-C. Liao, and M.Embrechts, Data mining techniquesapplied to medical information. Med. inform, 2000. 3. Obenshain, M.K., Application of Data Mining Techniques to Healthcare Data. Infection Control and Hospital Epidemiology, 2004. 4. Sandhya, J., et al.Classification of Neurodegenerative Disorders Based on Major Risk Factors Employing Machine Learning Techniques. International Journal of Engineering and Technology, 2010. Vol.2, No.4. 5. Thuraisingham, B., A Primer for Understanding and Applying Data Mining. IT Professional IEEE, 2000.

18. THANK YOU