Abdominal aortic aneurysm (AAA) is a localized dilatation of the abdominal aorta . It occurs when there is a increase in the normal diameter of the blood vessels by more than 50 percent. Approximately 90 percent of abdominal aortic aneurysms occur infrarenally , but they can also occur pararenally or suprarenally. This is because of some catastrophic outcome. Due to this, the blood flow is exaggerated so the blood hemodynamic interaction forces are affected. Therefore this will tends to wall rupture. To identify the AAA . it is important to identify the blood flow interaction and the wall shear stress. The blood and wall interaction is the wall shear stress. Computational fluid dynamics (CFD) is used to get the results for the mechanical conditions within the blood vessels with and without Aneurysms. CFD contains vast computations with Navier Stroke Equations so this will be very time consuming. So to make these CFD computations very efficient, Data mining (DM) techniques are to be used. And also DM techniques will be a best method to predict the shear stress at the AAA. This will estimate the wall shear stress. There is in need of thousands of CFD runs in a single computer for creating machine learning data so grid computing is used.

1. J. Cartmell, S. Enoch, D. Krstajic, and D. E. Leahy, "Automated QSPR through competitive workflow," J. Comput. Aided Mol. Des. , vol. 19, no. 11, pp. 821–833, Nov. 2005.
2. J. Cartmell, D. Krstajic, and D. E. Leahy, "Competitive workflow: Novel software architecture for automating drug design," Current Opinion Drug Discovery Develop. , vol. 10, no. 3, pp. 347–352, May 2007.
3. P. B. Dobrin, "Distribution Of Lamellar Deformation Implications For Properties Of The Arterial Media," Hypertension, Vol. 33, Pp. 806–810, 1999.
4. P. Z. Douglas, P. Libby, R. O. Bonow, And E. Braunwald, Braunwald's Heart Disease: A Textbook Of Cardiovascular Medicine.
5. N. Filipovic, S. Mijailovic, A. Tsuda, And M. Kojic, "An Implicit Algorithm Within The Arbitrary Lagrangian-Eulerian Formulation For Solving Incompressible Fluid Flow With Large Boundary Motions," Comput. Methods Appl. Mech. Eng. , Vol. 195, Pp. 6347–6361, 2006.
6. N. Filipovic, M. Kojic, M. Ivanovic, B. Stojanovic, L. Otasevic, And V. Rankovic, Medcfd, Specialized Cfd Software For Simulation Of Blood Flow Through Arteries. Kragujevac, Serbia: Univ. Kragujevac, 2006.
7. N. Filipovic, M. Kojic, M. Ivanovic, Anda. Tsuda, "Blood Flowand Thrombosis Modeling In Large Arteries Parallel Blood Flow Simulation (Pbfs)," Presented At The Bioinfogrid Symp. , Milan, Italy, Dec. 10–13, 2007.
8. M. F. Fillinger, S. P. Marra, M. L. Raghavan, And F. E. Kennedy, "Prediction Of Rupture Risk In Abdominal Aortic Aneurysm During Observation:Wall Stress Versus Diameter," J. Vascular Surg. , Vol. 37, No. 4, Pp. 724–732,2003.
9. S. Glagov, C. Zarins, D. P. Giddens, And D. N. Ku,"Hemodynamicsand Atherosclerosis Insights And Perspectives Gained From Studies Of Human Arteries," Archives Pathology Laboratory Med. , Vol. 112, Pp. 1018–1031, 1988.
10. Glite User Guide. (2009). [Online]. Available:Http://Glite. Web. Cern. Ch/Glite/Documentation/Default. Asp.
11. M. A. Hall, "In Correlation-Based Feature Selection Of Discrete And Numeric Class Machine Learning," In Proc. Int. Conf. Mach. Learning, San Francisco, Ca, 2000, Pp. 359–366.
12. M. Koji´C, N. Filipovi´C, B. Stojanovi´C, And N. Koji´C, Computer Modeling In Bioengineering. Hoboken, Nj: Wiley, 2008

Computational Fluid Dynamics (cfd) Data Mining (dm) Grid Computing Hemodynamic Parameters Predictive Modeling