| Peer-Reviewed

Predicting Diabetes Mellitus Using Artificial Neural Network Through a Simulation Study

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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[2] Indoria P. and Rathore Y. K., (2018). ASurvey: Detection and Prediction of Diabetes Using Machine Learning Techniques, International Journal of Engineering Research & Technology (IJERT), 7 (3), 287-291.
[3] Mamman M. and Saratha S. (2017), Predicting the survival of diabetes using neural network, AIP Conference Proceedings 1870, 040046; doi: 10.1063/1.4995878.
[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.
[5] Hagan, M. T., & Menhaj, M., (1994) “Training feed-forward networks with the Marquardt algorithm”, IEEE Trans. Neural Networks, Vol. 5, No. 6, pp 989-993.
[6] Livingstone, D., Totowa, NJ (2008). Artificial Neural Networks Methods and Application. 1st ed.: Hummana Pres.
[7] Dunne, RA., Wiley, J., Inc, S. (2007) "A Statistical Approach to Neural Networks for Pattern Recognition", New Jersey: John Wiley & Sons Inc.
[8] Pradhan M. and Sahu R. K., (2011) Predict the onset of diabetes disease using Artificial Neural Network (ANN), International Journal of Computer Science & Emerging Technologies, 2 (2), 303-311.
[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.
[11] Selvakumar S., Kannan K. S. and Nachiyar S. G. (2017). Prediction of Diabetes Diagnosis Using Classification Based Data Mining Techniques, International Journal of Statistics and Systems, 12 (2), 183-188.
[12] Wu H., Yang S., Huang Z., He J., and Wang X. (2018). Type 2 diabetes mellitus prediction model based on data mining, Informatics in Medicine Unlocked, 10, 100-107.
[13] Quan Z., Kaiyang Q., Yamei L., Dehui Y., Ying J., and Hua T. (2018). Predicting Diabetes Mellitus With Machine Learning Techniques, Frontiers in genetics, 9, 1-10.
[14] Sareh M. and Amir J. (2019). An Artificial Neural Network Model to Diagnosis of Type II Diabetes, Journal of Research in Medical and Dental Science, 7 (1), 66-70.
[15] Shameem H. (2018). Prediction of Diabetes Based on Artificial Intelligence Technique, International Research Journal of Engineering and Technology (IRJET), 5 (11), 11-15.
Cite This Article
  • 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
  • Department of Mathematics Usmanu Danfodiyo University, Sokoto, Nigeria

  • Department of Mathematics Usmanu Danfodiyo University, Sokoto, Nigeria

  • Department of Mathematics Usmanu Danfodiyo University, Sokoto, Nigeria

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

  • Department of Mathematics Usmanu Danfodiyo University, Sokoto, Nigeria

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