Skip to main content

Advertisement

SpringerLink
Log in

Modified Mixture of Experts for Diabetes Diagnosis

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. The major objective of the paper is to be a guide for the readers, who want to develop an automated decision support system for detection of diabetics and subjects having risk factors of diabetes. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. Several different classification algorithms were tested and their performances in detection of diabetics were compared. The performance of the classification algorithms was illustrated on the Pima Indians diabetes data set. The present research demonstrated that the modified mixture of experts (MME) achieved diagnostic accuracies which were higher than that of the other automated diagnostic systems.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E., Adaptive mixtures of local experts. Neural Comput. 3:179–87, 1991. doi:10.1162/neco.1991.3.1.79.

    Article  Google Scholar 

  2. Chen, K., Xu, L., and Chi, H., Improved learning algorithms for mixture of experts in multiclass classification. Neural Netw. 12:91229–1252, 1999. doi:10.1016/S0893-6080(99)00043-X.

    Article  Google Scholar 

  3. Hong, X., and Harris, C. J., A mixture of experts network structure construction algorithm for modelling and control. Appl. Intell. 16:159–69, 2002. doi:10.1023/A:1012869427428.

    Article  MATH  Google Scholar 

  4. Jordan, M. I., and Jacobs, R. A., Hierarchical mixture of experts and the EM algorithm. Neural Comput. 6:2181–214, 1994. doi:10.1162/neco.1994.6.2.181.

    Article  Google Scholar 

  5. Übeyli, E. D., Wavelet/mixture of experts network structure for EEG signals classification. Expert Syst. Appl. 34:31954–1962, 2008. doi:10.1016/j.eswa.2007.02.006.

    Article  Google Scholar 

  6. Übeyli, E. D., Comparison of different classification algorithms in clinical decision-making. Expert Syst. 24:117–31, 2007. doi:10.1111/j.1468-0394.2007.00418.x.

    Article  Google Scholar 

  7. Chen, K., A connectionist method for pattern classification with diverse features. Pattern Recognit. Lett. 19:7545–558, 1998. doi:10.1016/S0167-8655(98)00055-5.

    Article  MATH  Google Scholar 

  8. Shanker, M. S., Using neural networks to predict the onset of diabetes mellitus. J. Chem. Inf. Comput. Sci. 36:35–41, 1996. doi:10.1021/ci950063e.

    Google Scholar 

  9. Lim, C. P., Harrison, R. F., and Kennedy, R. L., Application of autonomous neural network systems to medical pattern classification tasks. Artif. Intell. Med. 11:215–239, 1997. doi:10.1016/S0933-3657(97)00035-3.

    Article  Google Scholar 

  10. Park, J., and Edington, D. W., A sequential neural network model for diabetes prediction. Artif. Intell. Med. 23:277–293, 2001. doi:10.1016/S0933-3657(01)00086-0.

    Article  Google Scholar 

  11. Übeyli, E. D., Combining neural network models for automated diagnostic systems. J. Med. Syst. 30:6483–488, 2006. doi:10.1007/s10916-006-9034-z.

    Article  Google Scholar 

  12. Übeyli, E. D., A mixture of experts network structure for breast cancer diagnosis. J. Med. Syst. 29:5569–579, 2005. doi:10.1007/s10916-005-6112-6.

    Article  Google Scholar 

  13. Übeyli, E. D., Implementing wavelet transform/mixture of experts network for analysis of electrocardiogram beats. Expert Syst. 25:2150–162, 2008. doi:10.1111/j.1468-0394.2008.00444.x.

    Article  Google Scholar 

  14. Haykin, S., Neural networks: A Comprehensive Foundation. Macmillan, New York, 1994.

    MATH  Google Scholar 

  15. Chaudhuri, B. B., and Bhattacharya, U., Efficient training and improved performance of multilayer perceptron in pattern classification. Neurocomputing. 34:11–27, 2000. doi:10.1016/S0925-2312(00)00305-2.

    Article  MATH  Google Scholar 

  16. The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus, Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care. 25:Supplement 1S5–S20, 2002. doi:10.2337/diacare.25.2007.S5.

    Google Scholar 

  17. Engelgau, M. M., Diabetes diagnostic criteria and impaired glycemic states: evolving evidence base. Clin. Diabetes. 22:269–70, 2004. doi:10.2337/diaclin.22.2.69.

    Article  Google Scholar 

  18. Besser, G. M., Bodansky, H. J., and Cudworth, A. G., Clinical diabetes an illustrated text. Gower Medical Publishing, London, 1988.

    Google Scholar 

  19. Pima Indians diabetes database. http://www.cormactech.com/neunet

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Üniversitesi, 06530, Söğütözü, Ankara, Turkey

    Elif Derya Übeyli

Authors
  1. Elif Derya Übeyli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elif Derya Übeyli.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Übeyli, E.D. Modified Mixture of Experts for Diabetes Diagnosis. J Med Syst 33, 299–305 (2009). https://doi.org/10.1007/s10916-008-9191-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-008-9191-3

Keywords

Search

Navigation