Volume 7, Issue 4, August 2019, Page: 56-62
The Exchangeable Markov Multi-states Growth Process Incorporate with an Artificial Neural Network of Preterm Infants in an Incubator
Jean Pierre Namahoro, Department of Mathematics, Faculty of Statistics, China University of Geosciences (Wuhan), Wuhan, PR. China
Xiao Haijun, Department of Mathematics, Faculty of Statistics, China University of Geosciences (Wuhan), Wuhan, PR. China
Received: Aug. 6, 2019;       Accepted: Aug. 26, 2019;       Published: Oct. 9, 2019
DOI: 10.11648/j.sjams.20190704.12      View  45      Downloads  24
Abstract
The standard incubator used to monitor the development of preterm infants, with much attention for random optimization can interrupt the three main parameters (oxygen, environmental temperature, and humidity) responsible for preterm growth. The artificial neural network (ANN) has been recently proposed as a novel technique to control those parameters to provide a better and stabilized environment in an incubator. Unfortunately, this novel technique cannot continuously provide and indicate the update challenge of preterm growth. The objective of this paper is to apply a Markov multi-state growth process incorporates with multilayer feed-forward artificial neural network as an improved methodology to continuously control and provide an update of preterm growth in an incubator. The exchangeable Markov growth process, transition graph, and artificial neural network discussed on and applied in the designed incubator as methodology in paper and then make a joint density function of Markov multi-states growth process through multi-steps designed Algorithm to get the theoretical results. The updated measurements (weight, height, and head-perimeter) associated with controlled parameters used as input to the threshold logic unit (TLU) of ANN and then distinguish whether the growth process is abnormal or normal at each state. The summarized algorithm and multilayer feed-forward ANN utilized the panel data collected at Murunda hospital in Rwanda as input to show the application of improved methodology proposed in this paper, specifically, multi-state growth process of preterm infants across gender. As results, the continuous exchangeability of the growth process at each state has updated and may show abnormal or normal of growth process, and then sensors may notify these change through the joint density function of Markov multi-states growth process. Thus, improved methodology can increase the security and minimize time consumption in continuous monitoring growth process in an advanced way in time this idea has been implemented.
Keywords
Preterm Growth, Artificial Neural Network, Markov Multi-state Process, Incubator
To cite this article
Jean Pierre Namahoro, Xiao Haijun, The Exchangeable Markov Multi-states Growth Process Incorporate with an Artificial Neural Network of Preterm Infants in an Incubator, Science Journal of Applied Mathematics and Statistics. Vol. 7, No. 4, 2019, pp. 56-62. doi: 10.11648/j.sjams.20190704.12
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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