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.
Cite this paper
James, T. O. , Gulumbe, S. U. and Danbaba, A. (2018). Resilient Back-Propagation Algorithm in the Prediction of Mother to Child Transmission of HIV. Open Access Library Journal, 5, e4538. doi: http://dx.doi.org/10.4236/oalib.1104538.
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