Artificial Neural Network Modeling of Blood Flow through Stenosed Artery with Bypass graft

R. Bhardwaj; S.P Singh; M.M. Srivastava; J.K. Arora

doi:https://doi.org/10.14445/22315373/IJMTT-V19P505

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 19 | Number 1 | Year 2015 | Article Id. IJMTT-V19P505 | DOI : https://doi.org/10.14445/22315373/IJMTT-V19P505

Artificial Neural Network Modeling of Blood Flow through Stenosed Artery with Bypass graft

R. Bhardwaj, S.P Singh, M.M. Srivastava, J.K. Arora

Citation :

R. Bhardwaj, S.P Singh, M.M. Srivastava, J.K. Arora, "Artificial Neural Network Modeling of Blood Flow through Stenosed Artery with Bypass graft," International Journal of Mathematics Trends and Technology (IJMTT), vol. 19, no. 1, pp. 34-38, 2015. Crossref, https://doi.org/10.14445/22315373/IJMTT-V19P505

Abstract

Artificial Neural Network model was designed to describe the behavior of blood flow in which degree of stenosis, flow rate, pressure anastomotic angle are considered as input variable while pressure and flow rate as output. Model predicts higher degree blockage of the stenosed artery with higher drop of blood pressure at the stenosis region. Bypass surgery at optimized anastomotic angle is highly useful for regulating the blood pressure. ANN provides reasonable predictive performance in resemblance to the experimental values. The Levenberg–Marquardt algorithm (LMA) was found best of BP algorithms with a minimum mean squared error (MSE) for training and cross validation.

Keywords

Artificial Neural Network, Stenosis, Anastomotic angle, Pulsatile flow.

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