Prediction of stenosis behaviour in artery by neural network and multiple linear regressions

Blood flow analysis in the artery is a paramount study in the field of arterial stenosis evaluation. Studies conducted so far have reported the analysis of blood flow parameters using different techniques, but the regression analysis is not adequately used. Artificial neural network is a nonlinear and nonparametric approach. It uses back-propagation algorithm for regression analysis, which is effective as compared to statistical model that requires a higher domain of statistics for prediction. In our manuscript, twofold analyses of data are done. First phase involves the determination of blood flow parameters using physiological flow pulse generator. The second phase includes regression modelling. The inputs to the model were axial length from stenosis, radial distance, inlet velocity, mean pressure, density, viscosity, time, and degree of blockage. Output included dependent variables in the form of output as mean velocity, root-mean-square (RMS) velocity, turbulent intensity, mean frequency, RMS frequency, frequency of turbulent intensity, gate time mean, gate time RMS. The temperature, density, and viscosity conditions were kept constant for various degrees of blockages. It was followed by regression analysis of variables using conventional statistical and neural network approach. The result shows that the neural network model is more appropriate, because value of percentage of response variation of dependent variable is almost approaching unity as compared to statistical analysis.