Prediction of Zn concentration in human seminal plasma of Normospermia samples by Artificial Neural Networks (ANN)

Abstract

There has been an increasing interest in the evaluation of metal ion concentration, present in different body fluids. It is known that metal ions, especially zinc play vital role in the fertility of human semen. The main objective of the study is to evaluate the Zn concentration in Normospermia samples by Atomic absorption spectroscopy (AAS) and to predict the same by artificial neural network (ANN). Normospermia semen samples were collected from the patients who came to attend semen analysis at Bangalore assisted conception centre, Bangalore, India. Semen analysis was done according to World Health Organization (WHO) guidance. Atomic absorption spectroscopy was used to estimate the total Zn in these samples, while the Back propagation neural network algorithm (BPNN) was used to predict the Zn levels in these samples. Zinc concentration obtained by AAS and BPNN indicated that there was a good correlation between the estimated and predicted values and was also found to be statistically significant. The BPNN algorithm developed in this study could be used for the prediction of Zn concentration in human Normospermia samples. The algorithm could be further developed to predict the concentration of all the trace elements present in human seminal plasma of different infertile categories.