Noninvasive measurement of the human RBC concentration based on BP NN model

Abstract

In the present article, the BP neural network's arithmetic model is applied to the noninvasive detection of the concentration of the red blood cell of human body. Due to the use of BP neural network in the modeling and analysis of the dynamic spectrum data and the actual measured value of the red blood cell, the authors get a better result which refers to that the output value tracks the expected result very well. The related coefficient R can reach 0.993. When predicting the output value in the way of the BP neural network model, the maximal relative error is only 4.7%, average relative error is 2.1%, so the authors can say that it has more ideal prediction ability. The experimental result shows that the BP neural network model can be accurate in dealing with the nonlinear relation between the dynamic spectrum data and human erythrocyte practical value and it can make the method of noninvasive blood analysis more useful in clinical application. So it has a high application value.