MINING DATA FROM HEMODYNAMIC SIMULATIONS VIA MULTILAYER PERCEPTRON NEURAL NETWORK

Arterial geometry variability is present both within and across individuals. A multilayer perceptron neural network was proposed for mining data generated from computer simulations. The proposed approach was applied to analyze the influence of geometric parameters on maximal wall shear stress (MWSS) in the human carotid artery bifurcation. A parametric model was used for generating a set of observational data that contains the maximum wall shear stress values for a range of probable arterial geometries. The data set was mined via a multilayer perceptron making it possible to predict values of the maximum wall shear stress for new geometries that were not in the training data set.