Resilient Back-Propagation Algorithm in the Prediction of Mother to Child Transmission of HIV

Abstract

Prediction of a child HIV status poses real challenges in medical research. Even though there are different statistical techniques and machine learning algorithms that have been used to predict models like HIV for the clinical data with binary outcome variables, yet neural network techniques are major participants for prediction purposes. HIV is the primary cause of mortality among women of reproductive age globally and is a key contributor to maternal, infant and child morbidity and mortality. In this paper, resilient back propagation algorithm is used for training the Neural Network and Multilayer Feed forward network to predict the mother to child transmission of HIV status.