A Classification Model for Severity of Neonatal Jaundice Using Deep Learning

Ngozi Chidozie Egejuru; Adanze Onyenonachi Asinobi; Oluwasina Adewunmi; Temilade Aderounmu; Samuel Ademola Adegoke; Peter Adebayo Idowu

doi:doi:10.11648/j.ajp.20190503.24

A Classification Model for Severity of Neonatal Jaundice Using Deep Learning

Ngozi Chidozie Egejuru, Adanze Onyenonachi Asinobi, Oluwasina Adewunmi, Temilade Aderounmu, Samuel Ademola Adegoke, Peter Adebayo Idowu

Published in American Journal of Pediatrics (Volume 5, Issue 3)

Received: 14 July 2019 Accepted: 5 August 2019 Published: 28 August 2019

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Abstract

Neonatal jaundice is a yellowish discoloration of the white part of the eyes and skin in a newborn baby due to high bilirubin levels. An early diagnosis of the severity of neonatal jaundice using machine learning will decrease neonates’ likelihood of developing complications. The study elicited knowledge on the variables that are associated with the severity of neonatal jaundice and collected relevant data from a tertiary hospital in south-western Nigeria. The study formulated the predictive model for the severity of neonatal jaundice based on the variables identified using deep learning with multi-layer perceptron (MLP) classifier for varying number of epochs. The results of the study showed that using the deep learning with MLP classifier and 5 epochs had the lowest error rate however with the highest build time and provided a better model compared to the use of the other number of epochs. The study concluded that the using deep learning with MLP classifier and 5 epochs, the development of the classification model for the severity of neonatal jaundice patients receiving treatment was more effective due to its ability to understand the relationship between the attributes and their respective target class labels.

Published in	American Journal of Pediatrics (Volume 5, Issue 3)
DOI	10.11648/j.ajp.20190503.24
Page(s)	159-169
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Neonatal Jaundice, Fuzzy Model, Risk Classification, Risk Factors

References

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APA Style

Ngozi Chidozie Egejuru, Adanze Onyenonachi Asinobi, Oluwasina Adewunmi, Temilade Aderounmu, Samuel Ademola Adegoke, et al. (2019). A Classification Model for Severity of Neonatal Jaundice Using Deep Learning. American Journal of Pediatrics, 5(3), 159-169. https://doi.org/10.11648/j.ajp.20190503.24

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ACS Style

Ngozi Chidozie Egejuru; Adanze Onyenonachi Asinobi; Oluwasina Adewunmi; Temilade Aderounmu; Samuel Ademola Adegoke, et al. A Classification Model for Severity of Neonatal Jaundice Using Deep Learning. Am. J. Pediatr. 2019, 5(3), 159-169. doi: 10.11648/j.ajp.20190503.24

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AMA Style

Ngozi Chidozie Egejuru, Adanze Onyenonachi Asinobi, Oluwasina Adewunmi, Temilade Aderounmu, Samuel Ademola Adegoke, et al. A Classification Model for Severity of Neonatal Jaundice Using Deep Learning. Am J Pediatr. 2019;5(3):159-169. doi: 10.11648/j.ajp.20190503.24

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@article{10.11648/j.ajp.20190503.24,
  author = {Ngozi Chidozie Egejuru and Adanze Onyenonachi Asinobi and Oluwasina Adewunmi and Temilade Aderounmu and Samuel Ademola Adegoke and Peter Adebayo Idowu},
  title = {A Classification Model for Severity of Neonatal Jaundice Using Deep Learning},
  journal = {American Journal of Pediatrics},
  volume = {5},
  number = {3},
  pages = {159-169},
  doi = {10.11648/j.ajp.20190503.24},
  url = {https://doi.org/10.11648/j.ajp.20190503.24},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajp.20190503.24},
  abstract = {Neonatal jaundice is a yellowish discoloration of the white part of the eyes and skin in a newborn baby due to high bilirubin levels. An early diagnosis of the severity of neonatal jaundice using machine learning will decrease neonates’ likelihood of developing complications. The study elicited knowledge on the variables that are associated with the severity of neonatal jaundice and collected relevant data from a tertiary hospital in south-western Nigeria. The study formulated the predictive model for the severity of neonatal jaundice based on the variables identified using deep learning with multi-layer perceptron (MLP) classifier for varying number of epochs. The results of the study showed that using the deep learning with MLP classifier and 5 epochs had the lowest error rate however with the highest build time and provided a better model compared to the use of the other number of epochs. The study concluded that the using deep learning with MLP classifier and 5 epochs, the development of the classification model for the severity of neonatal jaundice patients receiving treatment was more effective due to its ability to understand the relationship between the attributes and their respective target class labels.},
 year = {2019}
}

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TY  - JOUR
T1  - A Classification Model for Severity of Neonatal Jaundice Using Deep Learning
AU  - Ngozi Chidozie Egejuru
AU  - Adanze Onyenonachi Asinobi
AU  - Oluwasina Adewunmi
AU  - Temilade Aderounmu
AU  - Samuel Ademola Adegoke
AU  - Peter Adebayo Idowu
Y1  - 2019/08/28
PY  - 2019
N1  - https://doi.org/10.11648/j.ajp.20190503.24
DO  - 10.11648/j.ajp.20190503.24
T2  - American Journal of Pediatrics
JF  - American Journal of Pediatrics
JO  - American Journal of Pediatrics
SP  - 159
EP  - 169
PB  - Science Publishing Group
SN  - 2472-0909
UR  - https://doi.org/10.11648/j.ajp.20190503.24
AB  - Neonatal jaundice is a yellowish discoloration of the white part of the eyes and skin in a newborn baby due to high bilirubin levels. An early diagnosis of the severity of neonatal jaundice using machine learning will decrease neonates’ likelihood of developing complications. The study elicited knowledge on the variables that are associated with the severity of neonatal jaundice and collected relevant data from a tertiary hospital in south-western Nigeria. The study formulated the predictive model for the severity of neonatal jaundice based on the variables identified using deep learning with multi-layer perceptron (MLP) classifier for varying number of epochs. The results of the study showed that using the deep learning with MLP classifier and 5 epochs had the lowest error rate however with the highest build time and provided a better model compared to the use of the other number of epochs. The study concluded that the using deep learning with MLP classifier and 5 epochs, the development of the classification model for the severity of neonatal jaundice patients receiving treatment was more effective due to its ability to understand the relationship between the attributes and their respective target class labels.
VL  - 5
IS  - 3
ER  -

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Author Information

Ngozi Chidozie Egejuru

Department of Computer Science, Hallmark University, Ijebu-Itele, Nigeria
Adanze Onyenonachi Asinobi

Department of Pediatrics, College of Medicine, University of Ibadan, Ibadan, Nigeria
Oluwasina Adewunmi

Tai Solarin University of Education, Ijebu Ode, Nigeria
Temilade Aderounmu

Department of Pediatrics and Child Health Care, Obafemi Awolowo University Teaching Hospital Complex, Ile-Ife, Nigeria
Samuel Ademola Adegoke

Department of Pediatrics and Child Health Care, Obafemi Awolowo University, Ile-Ife, Nigeria
Peter Adebayo Idowu

Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

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APA Style

Ngozi Chidozie Egejuru, Adanze Onyenonachi Asinobi, Oluwasina Adewunmi, Temilade Aderounmu, Samuel Ademola Adegoke, et al. (2019). A Classification Model for Severity of Neonatal Jaundice Using Deep Learning. American Journal of Pediatrics, 5(3), 159-169. https://doi.org/10.11648/j.ajp.20190503.24

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ACS Style

Ngozi Chidozie Egejuru; Adanze Onyenonachi Asinobi; Oluwasina Adewunmi; Temilade Aderounmu; Samuel Ademola Adegoke, et al. A Classification Model for Severity of Neonatal Jaundice Using Deep Learning. Am. J. Pediatr. 2019, 5(3), 159-169. doi: 10.11648/j.ajp.20190503.24

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AMA Style

Ngozi Chidozie Egejuru, Adanze Onyenonachi Asinobi, Oluwasina Adewunmi, Temilade Aderounmu, Samuel Ademola Adegoke, et al. A Classification Model for Severity of Neonatal Jaundice Using Deep Learning. Am J Pediatr. 2019;5(3):159-169. doi: 10.11648/j.ajp.20190503.24

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@article{10.11648/j.ajp.20190503.24,
  author = {Ngozi Chidozie Egejuru and Adanze Onyenonachi Asinobi and Oluwasina Adewunmi and Temilade Aderounmu and Samuel Ademola Adegoke and Peter Adebayo Idowu},
  title = {A Classification Model for Severity of Neonatal Jaundice Using Deep Learning},
  journal = {American Journal of Pediatrics},
  volume = {5},
  number = {3},
  pages = {159-169},
  doi = {10.11648/j.ajp.20190503.24},
  url = {https://doi.org/10.11648/j.ajp.20190503.24},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajp.20190503.24},
  abstract = {Neonatal jaundice is a yellowish discoloration of the white part of the eyes and skin in a newborn baby due to high bilirubin levels. An early diagnosis of the severity of neonatal jaundice using machine learning will decrease neonates’ likelihood of developing complications. The study elicited knowledge on the variables that are associated with the severity of neonatal jaundice and collected relevant data from a tertiary hospital in south-western Nigeria. The study formulated the predictive model for the severity of neonatal jaundice based on the variables identified using deep learning with multi-layer perceptron (MLP) classifier for varying number of epochs. The results of the study showed that using the deep learning with MLP classifier and 5 epochs had the lowest error rate however with the highest build time and provided a better model compared to the use of the other number of epochs. The study concluded that the using deep learning with MLP classifier and 5 epochs, the development of the classification model for the severity of neonatal jaundice patients receiving treatment was more effective due to its ability to understand the relationship between the attributes and their respective target class labels.},
 year = {2019}
}

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TY  - JOUR
T1  - A Classification Model for Severity of Neonatal Jaundice Using Deep Learning
AU  - Ngozi Chidozie Egejuru
AU  - Adanze Onyenonachi Asinobi
AU  - Oluwasina Adewunmi
AU  - Temilade Aderounmu
AU  - Samuel Ademola Adegoke
AU  - Peter Adebayo Idowu
Y1  - 2019/08/28
PY  - 2019
N1  - https://doi.org/10.11648/j.ajp.20190503.24
DO  - 10.11648/j.ajp.20190503.24
T2  - American Journal of Pediatrics
JF  - American Journal of Pediatrics
JO  - American Journal of Pediatrics
SP  - 159
EP  - 169
PB  - Science Publishing Group
SN  - 2472-0909
UR  - https://doi.org/10.11648/j.ajp.20190503.24
AB  - Neonatal jaundice is a yellowish discoloration of the white part of the eyes and skin in a newborn baby due to high bilirubin levels. An early diagnosis of the severity of neonatal jaundice using machine learning will decrease neonates’ likelihood of developing complications. The study elicited knowledge on the variables that are associated with the severity of neonatal jaundice and collected relevant data from a tertiary hospital in south-western Nigeria. The study formulated the predictive model for the severity of neonatal jaundice based on the variables identified using deep learning with multi-layer perceptron (MLP) classifier for varying number of epochs. The results of the study showed that using the deep learning with MLP classifier and 5 epochs had the lowest error rate however with the highest build time and provided a better model compared to the use of the other number of epochs. The study concluded that the using deep learning with MLP classifier and 5 epochs, the development of the classification model for the severity of neonatal jaundice patients receiving treatment was more effective due to its ability to understand the relationship between the attributes and their respective target class labels.
VL  - 5
IS  - 3
ER  -

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