Skip to main content

A Prediction Model to Diabetes Using Artificial Metaplasticity

  • Conference paper
New Challenges on Bioinspired Applications (IWINAC 2011)

Abstract

Diabetes is the most common disease nowadays in all populations and in all age groups. Different techniques of artificial intelligence has been applied to diabetes problem. This research proposed the artificial metaplasticity on multilayer perceptron (AMMLP) as prediction model for prediction of diabetes. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with other algorithms, recently proposed by other researchers,that were applied to the same database. The best result obtained so far with the AMMLP algorithm is 89.93%.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Mohamed, E.I., Linderm, R., Perriello, G., Di Daniele, N., Poppl, S.J., De Lorenzo, A.: Predicting type 2 diabetes using an electronic nose-base artificial neural network analysis. Diabetes Nutrition & Metabolism 15(4), 215–221 (2002)

    Google Scholar 

  2. Shaw, J.E., Sicree, R.A., Zimmet, P.Z.: Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Research and Clinical Practice 87, 4–14 (2010), doi:10.1016/j.diabres.2009.10.007

    Article  Google Scholar 

  3. American diabetes asociation, http://www.diabetes.org/diabetes-basics/

  4. International Diabetes Federation, http://www.idf.org

  5. Temurtas, H., Yumusak, N., Temurtas, F.: A comparative study on diabetes disease diagnosis using neural networks. Expert Systems with Applications 36(4), 8610–8615 (2009), doi:10.1016/j.eswa.2008.10.032

    Article  Google Scholar 

  6. Polat, K., Gunes, S., Aslan, A.: A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine. Expert Systems with Applications 34(1), 214–221 (2008), doi:10.1016/j.eswa.2006.09.012

    Article  Google Scholar 

  7. Polat, K., Gunes, S.: An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digital Signal Processing 17(4), 702–710 (2007)

    Article  Google Scholar 

  8. UCI machine learning respiratory, http://archive.ics.uci.edu/ml/datasets.html .

  9. Abraham, W.C.: Activity-dependent regulation of synaptic plasticity(metaplasticity) in the hippocampus. In: Kato, N. (ed.) The Hippocampus: Functions and Clinical Relevance, pp. 15–26. Elsevier Science, Amsterdam (1996)

    Google Scholar 

  10. Kinto, E., Del-Moral-Hernandez, E., Marcano-Cedeño, A., Ropero-Peláez, J.: A preliminary neural model for movement direction recognition based on biologically plausible plasticity rules. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4528, pp. 628–636. Springer, Heidelberg (2007), doi:10.1007/978-3-540- 73055-2_65

    Google Scholar 

  11. Andina, D., Alvarez-Vellisco, A., Jevtić, A., Fombellida, J.: Artificial metaplasticity can improve artificial neural network learning. Intelligent Automation and Soft Computing; Special Issue in Signal Processing and Soft Computing, 15(4), 681-694 (2009); ISSN: 1079-8587

    Google Scholar 

  12. Marcano-Cedeño, A., Quintanilla-Domínguez, J., Andina, D.: Breast cancer classification applying artificial metaplasticity algorithm. Neurocomputing, doi:10.1016/j.neucom.2010.07.019

    Google Scholar 

  13. Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 27, 379–423 (1948), doi:10.1145/584091.584093

    Article  MathSciNet  MATH  Google Scholar 

  14. Hagan, M.T., Demuth, H.B., Beale, M.: Neural network design. PWS Pub. Co., Boston (1996)

    Google Scholar 

  15. Leung, H., Haykin, S.: The complex backpropagation algorithm. IEEE Transactions on Signal Processing 39, 2101–2104 (1991)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Marcano-Cedeño, A., Torres, J., Andina, D. (2011). A Prediction Model to Diabetes Using Artificial Metaplasticity. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21326-7_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21325-0

  • Online ISBN: 978-3-642-21326-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics