Skip to main content

Diabetes Mellitus Risk Prediction Using Artificial Neural Network

  • Conference paper
  • First Online:
Proceedings of International Joint Conference on Computational Intelligence

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Diabetes is a non-communicable disease and various types of dangerous diseases like heart attack, kidney failure, myopia, and so on are caused by it. The number of people suffering from diabetes is increasing rapidly. Though there has no perpetual cure for diabetes, it can be controlled by proper counseling and medication. For this perception, an early determination is needed. In our analysis, 464 patients data with 23 features were collected from various health-care units and preprocessed. A predictive model was developed with artificial neural network technique. Different learning rate, hidden layers were applied in our analysis. Average-weighted accuracy of all observations was approximately 99.69%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. “Islets of Langerhans \(\mid \) Definition, Function, Location, Facts”, Encyclopedia britannicam, (2018). https://www.britannica.com/science/islets-of-Langerhans. Accessed 07 Aug 2018

  2. “Diabetes”, World Health Organization, (2018). http://www.who.int/news-room/fact-sheets/detail/diabetes. Accessed 12 Sept 2018

  3. Mahbub I (2016) The state of diabetes in Bangladesh-future startup. In: Future startup. https://futurestartup.com/2016/07/27/the-state-of-diabetes-in-bangladesh/. Accessed 12 Aug 2018

  4. Shariful Islam S, Lechner A, Ferrari U, Laxy M, Seissler J, Brown J, Niessen LW, Holle R (2017) Healthcare use and expenditure for diabetes in Bangladesh. In: BMJ global health, vol 2, no 1, pp 1–6

    Google Scholar 

  5. “IDF Sea Members”, International diabetes federation. https://www.idf.org/our-network/regions-members/south-east-asia/members/93-bangladesh.html. Accessed 12 Aug 2018

  6. “Types of Diabetes Mellitus”, WebMD. https://www.webmd.com/diabetes/guide/types-of-diabetes-mellitus. Accessed 07 Oct 2018

  7. Lee B, Kim J (2016) Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning. IEEE J Biomed Health Inf 20(1):39–46 (Korea)

    Google Scholar 

  8. Rallapalli S, Suryakanthi T (2016) Predicting the risk of diabetes in big data electronic health Records by using scalable random forest classification algorithm. In: 2016 international conference on advances in computing and communication engineering (ICACCE), Durban, South Africa, pp 281–284

    Google Scholar 

  9. Songthung P, Sripanidkulchai K (2016) Improving type 2 diabetes mellitus risk prediction using classification. In: 13th international joint conference on computer science and software engineering (JCSSE), Pathumthani, Thailand, pp 1–6

    Google Scholar 

  10. Xu W, Zhang J, Zhang Q, Wei X (2017) Risk prediction of type II diabetes based on random forest model. In: 2017 third international conference on advances in electrical, electronics, information, communication and bio-informatics (AEEICB), Chennai, India, pp 382–386

    Google Scholar 

  11. AlThunayan L, AlSahdi N, Syed L (2017) Comparative analysis of different classification algorithms for prediction of diabetes disease. In: ICC’17 proceedings of the second international conference on internet of things, data and cloud computing, New York, USA, Article No 144

    Google Scholar 

  12. Komi M, Li J, Zhai Y, Zhang X (2018) Application of data mining methods in diabetes prediction. In: 2017 2nd international conference on image, vision and computing (ICIVC), Chengdu, China, pp 1006–1010

    Google Scholar 

  13. “What Is Backpropagation? \(\mid \) Training A Neural Network \(\mid \) Edureka”, Edureka Blog. https://www.edureka.co/blog/backpropagation/. Accessed 14 Oct 2018

  14. “CRAN-Package ROCR”, Cran.r-project.org, 2015. https://cran.r-project.org/web/packages/ROCR/index.html. Accessed 16 Oct 2018

  15. “Training of Neural Networks (R package neuralnet version 1.33)”, Cran.r-project.org, 2018. https://cran.r-project.org/web/packages/neuralnet/index.html. Accessed 19 Oct 2018

  16. “A Short Introduction to the Caret Package”, Cran.r-project.org. https://cran.r-project.org/web/packages/caret/vignettes/caret.html. Accessed 19 Oct 2018

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Raihan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Raihan, M., Alvi, N., Tanvir Islam, M., Farzana, F., Mahadi Hassan, M. (2020). Diabetes Mellitus Risk Prediction Using Artificial Neural Network. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3607-6_7

Download citation

Publish with us

Policies and ethics