Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic

Auwal Nata’ala; Hamman Dikko Muazu; Ibrahim Goni; Abdullahi Mohammed Jingi

doi:doi:10.11648/j.mcs.20190403.11

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Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic

Auwal Nata’ala, Hamman Dikko Muazu, Ibrahim Goni, Abdullahi Mohammed Jingi

Published in Mathematics and Computer Science (Volume 4, Issue 3)

Received: 9 March 2019 Accepted: 22 April 2019 Published: 12 October 2019

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Abstract

Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin. The main aim of this research work was to determine the blood glucose level of diabetic patient using adaptive Neuro-fuzzy. Data of 80 diabetic patients were collected from Federal Medical Centre Jalingo. It was used for training and testing the system, Gaussian Membership function was used, hybrid training algorithm was used for training and testing, the error obtain is 0.0008333 at epoch 4 which shows that the training performance is exactly 99.99% and testing performance of the system are 99.99% at epoch 4. This shows that adaptive Neuro-fuzzy system can be applied to medical diagnosis because of the error obtained.

Published in	Mathematics and Computer Science (Volume 4, Issue 3)
DOI	10.11648/j.mcs.20190403.11
Page(s)	63-67
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, Neuro-Fuzzy, Gaussian, Hybrid

References

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[2]	WHO (2010) Diabetic fact sheet.
[3]	Neeraj K. G., Anjali G. & Praveen K. T. (2014). Early Detection of Diabetes Patients using Soft Computing, IEEE International Conference on Issues and Challenges in Intelligent Computing Techniques.
[4]	Filipe F. & Henrique V. (2015). Artificial Neural Networks in Diabetes Control Science and Information Conference 2015July 28-30, 2015 London, UK.
[5]	Vaishali J. & Supriya R. (2015). Improving the Prediction Rate of Diabetes using Fuzzy Expert System International Journal of Information Technology and Computer Science, 10 (3), 84-91.
[6]	Tejashri N. G. & Satish R. T., (2015). Prognosis of Diabetes using Neural Network, Fuzzy Logic, Gaussian Kernel Method International Journal of Computer Applications, 124 (10), 3336.
[7]	Alby S. & Shivakumar BL (2018) A prediction model for type 2 diabetes using adaptive neuro-fuzzy interface system, Biomedical Research Computational Life Sciences and Smarter Technological Advancement ISSN 0970-938X.
[8]	Mahmoud R. S., Shahaboddin S., Shahram G., Teh Y., Aghabozorgi S., Laiha M., & Valentina E., (2015). RAIRS2 A new Expert System for Diagnosing Tuberculosis with Real world Tournament Selection Mechanism inside Artificial Immune recognition system, International Federation for Medical and Biological Engineering Springer, 8 (4), 11-19.
[9]	Navneet W., Sharad K. T. & Rahul M., (2015). Design and Identification of Tuberculosis using Fuzzy Based Decision Support System Advances in Computer Science and Information Technology (ACSIT), 2 (8), 57-62.
[10]	Yuanda L., David P. & Jorge M. (2015). A Web-based Fuzzy Inference System Based Tool for Cardiovascular Disease Risk Assessment NOVA. 13 (24), 7-16.
[11]	Zeinab A. & Hamid T. (2015). Design of a Fuzzy Expert System and a Multi-Layer Neural Network System For Diagnosis of Hypertension Bulletin of Environment, Pharmacology and Life Sciences, 4 (15), 138-145.
[12]	Richard A., Joseph K. P. & Kwabena R. (2015) Implementation of Adaptive Neuro Fuzzy Inference System for Malaria Diagnosis (Case Study: Kwesimintsim Polyclinic), International Journal of Computer Applications. 115 (7): 33-37.
[13]	Iryna P. et al. (2016). Neo-Fuzzy Approach for Medical Diagnostics Tasks in Online-Mode, IEEE First International Conference on Data Stream Mining and Processing 23-27 August 2016, Ukraine, 34-37.
[14]	Rupali Z., & Jyoti A., (2016). Pre-Prediction of Tuberculosis Disease Using Soft Computing Technique International Journal of Advanced Research in Computer and Communication Engineering, 5 (6), 56-61.
[15]	Shashank B., Praveen K. & Amit K. G. (2016). Neuro-fuzzy expert system in e-Health Monitoring for Disease Diagnosis, International Journal of Innovations in Engineering and Technology 7 (2): 249-252.
[16]	Ibrahim G., Jerome M. G. & Timothy U. M. (2017). Designing a Neuro-Fuzzy Model for Predicting the Existence of Mycobacterium Tuberculosis 36^th Annual Conference of the Nigerian Mathematical Society (NMS) 9^th – 12^th May 2017 held at University of Agriculture Makurdi, Benue State.
[17]	Jerome M. G, Ibrahim Goni & Timothy U. M. (2017) Adaptive Neuro-fuzzy system for Determining the Severity Level of Osteomyelitis and Control, Archives of Applied Science Research, 9 (2), 9-15.
[18]	Jerome M. G., Ibrahim G. & Mohammed I. (2018). Neuro-Fuzzy Approach for Diagnosing and Control of Tuberculosis the International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) 5 (1), 1-10.
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APA Style

Auwal Nata’ala, Hamman Dikko Muazu, Ibrahim Goni, Abdullahi Mohammed Jingi. (2019). Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic. Mathematics and Computer Science, 4(3), 63-67. https://doi.org/10.11648/j.mcs.20190403.11

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ACS Style

Auwal Nata’ala; Hamman Dikko Muazu; Ibrahim Goni; Abdullahi Mohammed Jingi. Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic. Math. Comput. Sci. 2019, 4(3), 63-67. doi: 10.11648/j.mcs.20190403.11

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AMA Style

Auwal Nata’ala, Hamman Dikko Muazu, Ibrahim Goni, Abdullahi Mohammed Jingi. Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic. Math Comput Sci. 2019;4(3):63-67. doi: 10.11648/j.mcs.20190403.11

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@article{10.11648/j.mcs.20190403.11,
  author = {Auwal Nata’ala and Hamman Dikko Muazu and Ibrahim Goni and Abdullahi Mohammed Jingi},
  title = {Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic},
  journal = {Mathematics and Computer Science},
  volume = {4},
  number = {3},
  pages = {63-67},
  doi = {10.11648/j.mcs.20190403.11},
  url = {https://doi.org/10.11648/j.mcs.20190403.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20190403.11},
  abstract = {Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin. The main aim of this research work was to determine the blood glucose level of diabetic patient using adaptive Neuro-fuzzy. Data of 80 diabetic patients were collected from Federal Medical Centre Jalingo. It was used for training and testing the system, Gaussian Membership function was used, hybrid training algorithm was used for training and testing, the error obtain is 0.0008333 at epoch 4 which shows that the training performance is exactly 99.99% and testing performance of the system are 99.99% at epoch 4. This shows that adaptive Neuro-fuzzy system can be applied to medical diagnosis because of the error obtained.},
 year = {2019}
}

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TY  - JOUR
T1  - Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic
AU  - Auwal Nata’ala
AU  - Hamman Dikko Muazu
AU  - Ibrahim Goni
AU  - Abdullahi Mohammed Jingi
Y1  - 2019/10/12
PY  - 2019
N1  - https://doi.org/10.11648/j.mcs.20190403.11
DO  - 10.11648/j.mcs.20190403.11
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 63
EP  - 67
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20190403.11
AB  - Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin. The main aim of this research work was to determine the blood glucose level of diabetic patient using adaptive Neuro-fuzzy. Data of 80 diabetic patients were collected from Federal Medical Centre Jalingo. It was used for training and testing the system, Gaussian Membership function was used, hybrid training algorithm was used for training and testing, the error obtain is 0.0008333 at epoch 4 which shows that the training performance is exactly 99.99% and testing performance of the system are 99.99% at epoch 4. This shows that adaptive Neuro-fuzzy system can be applied to medical diagnosis because of the error obtained.
VL  - 4
IS  - 3
ER  -

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Author Information

Auwal Nata’ala

Department of Computer Science, Faculty of Science Federal Polytechnic, Kaura-Namoda, Nigeria
Hamman Dikko Muazu

Department of Operation Research, Faculty of Pure and Applied Science, Modibbo Adama University of Technology, Yola, Nigeria
Ibrahim Goni

Department of Computer Science, Faculty of Science Adamawa State University, Mubi, Nigeria
Abdullahi Mohammed Jingi

Department of Computer Science, Faculty of Science Adamawa State University, Mubi, Nigeria

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Plain Text BibTeX RIS

APA Style

Auwal Nata’ala, Hamman Dikko Muazu, Ibrahim Goni, Abdullahi Mohammed Jingi. (2019). Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic. Mathematics and Computer Science, 4(3), 63-67. https://doi.org/10.11648/j.mcs.20190403.11

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ACS Style

Auwal Nata’ala; Hamman Dikko Muazu; Ibrahim Goni; Abdullahi Mohammed Jingi. Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic. Math. Comput. Sci. 2019, 4(3), 63-67. doi: 10.11648/j.mcs.20190403.11

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AMA Style

Auwal Nata’ala, Hamman Dikko Muazu, Ibrahim Goni, Abdullahi Mohammed Jingi. Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic. Math Comput Sci. 2019;4(3):63-67. doi: 10.11648/j.mcs.20190403.11

Copy | Download

@article{10.11648/j.mcs.20190403.11,
  author = {Auwal Nata’ala and Hamman Dikko Muazu and Ibrahim Goni and Abdullahi Mohammed Jingi},
  title = {Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic},
  journal = {Mathematics and Computer Science},
  volume = {4},
  number = {3},
  pages = {63-67},
  doi = {10.11648/j.mcs.20190403.11},
  url = {https://doi.org/10.11648/j.mcs.20190403.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20190403.11},
  abstract = {Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin. The main aim of this research work was to determine the blood glucose level of diabetic patient using adaptive Neuro-fuzzy. Data of 80 diabetic patients were collected from Federal Medical Centre Jalingo. It was used for training and testing the system, Gaussian Membership function was used, hybrid training algorithm was used for training and testing, the error obtain is 0.0008333 at epoch 4 which shows that the training performance is exactly 99.99% and testing performance of the system are 99.99% at epoch 4. This shows that adaptive Neuro-fuzzy system can be applied to medical diagnosis because of the error obtained.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic
AU  - Auwal Nata’ala
AU  - Hamman Dikko Muazu
AU  - Ibrahim Goni
AU  - Abdullahi Mohammed Jingi
Y1  - 2019/10/12
PY  - 2019
N1  - https://doi.org/10.11648/j.mcs.20190403.11
DO  - 10.11648/j.mcs.20190403.11
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 63
EP  - 67
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20190403.11
AB  - Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin. The main aim of this research work was to determine the blood glucose level of diabetic patient using adaptive Neuro-fuzzy. Data of 80 diabetic patients were collected from Federal Medical Centre Jalingo. It was used for training and testing the system, Gaussian Membership function was used, hybrid training algorithm was used for training and testing, the error obtain is 0.0008333 at epoch 4 which shows that the training performance is exactly 99.99% and testing performance of the system are 99.99% at epoch 4. This shows that adaptive Neuro-fuzzy system can be applied to medical diagnosis because of the error obtained.
VL  - 4
IS  - 3
ER  -

Copy | Download