Abstract
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 algorithms are to be used. And also DM algorithms 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 can be used.
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Kumar, A.D., Nidhya, R., Hanah Ayisha, V., Manokar, V., Manokar, N.V. (2014). Identification of Abdominal Aorta Aneurysm Using Ant Colony Optimization Algorithm. In: Satapathy, S., Avadhani, P., Udgata, S., Lakshminarayana, S. (eds) ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I. Advances in Intelligent Systems and Computing, vol 248. Springer, Cham. https://doi.org/10.1007/978-3-319-03107-1_6
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DOI: https://doi.org/10.1007/978-3-319-03107-1_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03106-4
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