Journal of Medical Systems

, Volume 33, Issue 4, pp 299–305 | Cite as

Modified Mixture of Experts for Diabetes Diagnosis

Original Paper


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.


Automated diagnostic systems Decision support system Modified mixture of experts Diabetes diagnosis 


  1. 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.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. 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.MATHCrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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.MATHCrossRefGoogle Scholar
  8. 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. 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.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. 14.
    Haykin, S., Neural networks: A Comprehensive Foundation. Macmillan, New York, 1994.MATHGoogle Scholar
  15. 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.MATHCrossRefGoogle Scholar
  16. 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. 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.CrossRefGoogle Scholar
  18. 18.
    Besser, G. M., Bodansky, H. J., and Cudworth, A. G., Clinical diabetes an illustrated text. Gower Medical Publishing, London, 1988.Google Scholar
  19. 19.
    Pima Indians diabetes database.

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of Electrical and Electronics Engineering, Faculty of EngineeringTOBB Ekonomi ve Teknoloji ÜniversitesiSöğütözüTurkey

Personalised recommendations