Prediction of blood screening parameters for preliminary analysis using neural networks

Over time, the latest technologies and advancements in prediction algorithms are unmasking the innovational ways of diagnosing, predicting, and cure of diseases related to the blood. For this, artificial intelligence and neural networks have played a significant role. Monitoring blood parameters plays a vital role in detecting dormant diseases or any kind of complications. This chapter deals with the various proposed techniques used in the prediction of blood parameters. This chapter mainly presents the forecast of blood screening test features using the backpropagation neural network. The elements used in this paper are fibrinogen and globulin. The normal ranges of fibrinogen and globulin are 2–4 g/L and 20–35 g/L. The glycoprotein which circulates in the vertebrates of the human body is known as fibrinogen. Different congenital and disorders related to the acquired fibrinogen in humans can lead to dysfunctional or reduced fibrinogens. In the human body, the proteins in the blood except albumin are collectively known as globulin. Sixty percent of the proteins in the human body comprises of albumin. Various antibodies, enzymes, and all other categories of proteins fall under the globulins.