Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study

Shehu Usman Gulumbe; Shamsuddeen Suleiman; Shehu Badamasi; Ahmad Yusuf Tambuwal; Umar Usman

doi:doi:10.11648/j.mlr.20190402.12

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Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study

Shehu Usman Gulumbe, Shamsuddeen Suleiman, Shehu Badamasi, Ahmad Yusuf Tambuwal, Umar Usman

Published in Machine Learning Research (Volume 4, Issue 2)

Received: 24 July 2019 Accepted: 16 August 2019 Published: 2 September 2019

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Abstract

Diabetes mellitus (DM) is a diverse group of metabolic disorders that is frequently associated with a high disease burden in developing countries such as Nigeria. It also needs continuous blood glucose monitoring and self-management. This research is aimed to predict diabetes mellitus using artificial neural network. In this research, 100 patients were considered from Ahmadu Bello University Teaching Hospital who have undergone diabetes screening test and 29 risk factors were used. Back propagation algorithm was used to train the artificial neural network for the original and simulated data sets. The results show that the models achieved 98.7%, 57.0%, 73.3%, and 63.0% accuracy for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The results also shows that the areas covered under receiver operating curves are 0.997, 0.587, 0.849 and 0.706 for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The research therefore concludes that in order to predict diabetes mellitus in patients, the simulated data can be used in place of the original data since the simulated ANN models have been able to discriminate between diabetic and non-diabetic patients.

Published in	Machine Learning Research (Volume 4, Issue 2)
DOI	10.11648/j.mlr.20190402.12
Page(s)	33-38
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

Diabetes Mellitus, Backpropagation, Simulation, Prediction, Artificial Neural Network

References

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[4]	Natchiar S. U. and Baulkani S. (2018). Review of Diabetes Disease Diagnosis Using Data Mining and Soft Computing Techniques, International Journal of Pure and Applied Mathematics, 118 (10), 137-142.
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[9]	Mishra V., Samuel C. and Sharma S. K. (2015). Use of Machine Learning to Predict the Onset of Diabetes. International Journal of Recent advances in Mechanical Engineering, 4 (2), 9-14.
[10]	Saiti K., Macas M., Stechova K., Pithova P., and Lhotska L. (2017). A review of model prediction in diabetes and of designing glucose regulators based on model predictive control for the artificial pancreas, Biomedical Engineering Education, 2-4.
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APA Style

Shehu Usman Gulumbe, Shamsuddeen Suleiman, Shehu Badamasi, Ahmad Yusuf Tambuwal, Umar Usman. (2019). Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study. Machine Learning Research, 4(2), 33-38. https://doi.org/10.11648/j.mlr.20190402.12

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ACS Style

Shehu Usman Gulumbe; Shamsuddeen Suleiman; Shehu Badamasi; Ahmad Yusuf Tambuwal; Umar Usman. Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study. Mach. Learn. Res. 2019, 4(2), 33-38. doi: 10.11648/j.mlr.20190402.12

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AMA Style

Shehu Usman Gulumbe, Shamsuddeen Suleiman, Shehu Badamasi, Ahmad Yusuf Tambuwal, Umar Usman. Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study. Mach Learn Res. 2019;4(2):33-38. doi: 10.11648/j.mlr.20190402.12

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@article{10.11648/j.mlr.20190402.12,
  author = {Shehu Usman Gulumbe and Shamsuddeen Suleiman and Shehu Badamasi and Ahmad Yusuf Tambuwal and Umar Usman},
  title = {Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study},
  journal = {Machine Learning Research},
  volume = {4},
  number = {2},
  pages = {33-38},
  doi = {10.11648/j.mlr.20190402.12},
  url = {https://doi.org/10.11648/j.mlr.20190402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20190402.12},
  abstract = {Diabetes mellitus (DM) is a diverse group of metabolic disorders that is frequently associated with a high disease burden in developing countries such as Nigeria. It also needs continuous blood glucose monitoring and self-management. This research is aimed to predict diabetes mellitus using artificial neural network. In this research, 100 patients were considered from Ahmadu Bello University Teaching Hospital who have undergone diabetes screening test and 29 risk factors were used. Back propagation algorithm was used to train the artificial neural network for the original and simulated data sets. The results show that the models achieved 98.7%, 57.0%, 73.3%, and 63.0% accuracy for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The results also shows that the areas covered under receiver operating curves are 0.997, 0.587, 0.849 and 0.706 for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The research therefore concludes that in order to predict diabetes mellitus in patients, the simulated data can be used in place of the original data since the simulated ANN models have been able to discriminate between diabetic and non-diabetic patients.},
 year = {2019}
}

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TY  - JOUR
T1  - Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study
AU  - Shehu Usman Gulumbe
AU  - Shamsuddeen Suleiman
AU  - Shehu Badamasi
AU  - Ahmad Yusuf Tambuwal
AU  - Umar Usman
Y1  - 2019/09/02
PY  - 2019
N1  - https://doi.org/10.11648/j.mlr.20190402.12
DO  - 10.11648/j.mlr.20190402.12
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 33
EP  - 38
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20190402.12
AB  - Diabetes mellitus (DM) is a diverse group of metabolic disorders that is frequently associated with a high disease burden in developing countries such as Nigeria. It also needs continuous blood glucose monitoring and self-management. This research is aimed to predict diabetes mellitus using artificial neural network. In this research, 100 patients were considered from Ahmadu Bello University Teaching Hospital who have undergone diabetes screening test and 29 risk factors were used. Back propagation algorithm was used to train the artificial neural network for the original and simulated data sets. The results show that the models achieved 98.7%, 57.0%, 73.3%, and 63.0% accuracy for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The results also shows that the areas covered under receiver operating curves are 0.997, 0.587, 0.849 and 0.706 for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The research therefore concludes that in order to predict diabetes mellitus in patients, the simulated data can be used in place of the original data since the simulated ANN models have been able to discriminate between diabetic and non-diabetic patients.
VL  - 4
IS  - 2
ER  -

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Author Information

Shehu Usman Gulumbe

Department of Mathematics Usmanu Danfodiyo University, Sokoto, Nigeria
Shamsuddeen Suleiman

Department of Mathematics Usmanu Danfodiyo University, Sokoto, Nigeria
Shehu Badamasi

Department of Mathematics Usmanu Danfodiyo University, Sokoto, Nigeria
Ahmad Yusuf Tambuwal

Department of Information and Communication Technology, Usmanu Danfodiyo University, Sokoto, Nigeria
Umar Usman

Department of Mathematics Usmanu Danfodiyo University, Sokoto, Nigeria

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Plain Text BibTeX RIS

APA Style

Shehu Usman Gulumbe, Shamsuddeen Suleiman, Shehu Badamasi, Ahmad Yusuf Tambuwal, Umar Usman. (2019). Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study. Machine Learning Research, 4(2), 33-38. https://doi.org/10.11648/j.mlr.20190402.12

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ACS Style

Shehu Usman Gulumbe; Shamsuddeen Suleiman; Shehu Badamasi; Ahmad Yusuf Tambuwal; Umar Usman. Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study. Mach. Learn. Res. 2019, 4(2), 33-38. doi: 10.11648/j.mlr.20190402.12

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AMA Style

Shehu Usman Gulumbe, Shamsuddeen Suleiman, Shehu Badamasi, Ahmad Yusuf Tambuwal, Umar Usman. Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study. Mach Learn Res. 2019;4(2):33-38. doi: 10.11648/j.mlr.20190402.12

Copy | Download

@article{10.11648/j.mlr.20190402.12,
  author = {Shehu Usman Gulumbe and Shamsuddeen Suleiman and Shehu Badamasi and Ahmad Yusuf Tambuwal and Umar Usman},
  title = {Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study},
  journal = {Machine Learning Research},
  volume = {4},
  number = {2},
  pages = {33-38},
  doi = {10.11648/j.mlr.20190402.12},
  url = {https://doi.org/10.11648/j.mlr.20190402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20190402.12},
  abstract = {Diabetes mellitus (DM) is a diverse group of metabolic disorders that is frequently associated with a high disease burden in developing countries such as Nigeria. It also needs continuous blood glucose monitoring and self-management. This research is aimed to predict diabetes mellitus using artificial neural network. In this research, 100 patients were considered from Ahmadu Bello University Teaching Hospital who have undergone diabetes screening test and 29 risk factors were used. Back propagation algorithm was used to train the artificial neural network for the original and simulated data sets. The results show that the models achieved 98.7%, 57.0%, 73.3%, and 63.0% accuracy for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The results also shows that the areas covered under receiver operating curves are 0.997, 0.587, 0.849 and 0.706 for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The research therefore concludes that in order to predict diabetes mellitus in patients, the simulated data can be used in place of the original data since the simulated ANN models have been able to discriminate between diabetic and non-diabetic patients.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study
AU  - Shehu Usman Gulumbe
AU  - Shamsuddeen Suleiman
AU  - Shehu Badamasi
AU  - Ahmad Yusuf Tambuwal
AU  - Umar Usman
Y1  - 2019/09/02
PY  - 2019
N1  - https://doi.org/10.11648/j.mlr.20190402.12
DO  - 10.11648/j.mlr.20190402.12
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 33
EP  - 38
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20190402.12
AB  - Diabetes mellitus (DM) is a diverse group of metabolic disorders that is frequently associated with a high disease burden in developing countries such as Nigeria. It also needs continuous blood glucose monitoring and self-management. This research is aimed to predict diabetes mellitus using artificial neural network. In this research, 100 patients were considered from Ahmadu Bello University Teaching Hospital who have undergone diabetes screening test and 29 risk factors were used. Back propagation algorithm was used to train the artificial neural network for the original and simulated data sets. The results show that the models achieved 98.7%, 57.0%, 73.3%, and 63.0% accuracy for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The results also shows that the areas covered under receiver operating curves are 0.997, 0.587, 0.849 and 0.706 for training the original, simulated at 100, simulated at 150 and simulated at 200 data sets respectively. The research therefore concludes that in order to predict diabetes mellitus in patients, the simulated data can be used in place of the original data since the simulated ANN models have been able to discriminate between diabetic and non-diabetic patients.
VL  - 4
IS  - 2
ER  -

Copy | Download