Prediction of calcium concentration in human blood serum using an artificial neural network

Abstract

A predictive method, based on artificial neural network (ANN) has been developed to study absorbance and pH effects on the equilibrium of blood serum. This strategy has been used to analyze serum samples and to predict the calcium concentration in blood serum. A dedicated data acquisition system is designed and fabricated using a LPC2106 microcontroller with light emitting diode (LED) as source and photodiode as sensor to measure absorbance and to calculate the calcium concentration. A multilayer neural network with back propagation (BP) training algorithm is used to simulate different concentration of calcium (Ca 2+) as a function of absorbance and pH, to correlate and predict calcium concentration. The computed calcium concentration by neural network is quite satisfactory with correlations R 2 = 0.998 and 0.995, standard errors of 0.0127 and 0.0122 in validation and testing stages respectively. Statistical analysis are carried out to check the accuracy and precision of the proposed ANN model and validation of results produce a relative error of about 3%. These results suggest that ANN can be efficiently applied and is in good agreement with values obtained with the current clinical spectrophotometric methods. Hence, ANN can be used as a complementary tool for studying metal ion complexion, with special attention to the blood serum analysis.