Skip to main content

Advertisement

SpringerLink
Log in

Tuberculosis Disease Diagnosis Using Artificial Neural Network Trained with Genetic Algorithm

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

Abstract

Tuberculosis is a common and often deadly infectious disease caused by mycobacterium; in humans it is mainly Mycobacterium tuberculosis (Wikipedia 2009). It is a great problem for most developing countries because of the low diagnosis and treatment opportunities. Tuberculosis has the highest mortality level among the diseases caused by a single type of microorganism. Thus, tuberculosis is a great health concern all over the world, and in Turkey as well. This article presents a study on tuberculosis diagnosis, carried out with the help of multilayer neural networks (MLNNs). For this purpose, an MLNN with two hidden layers and a genetic algorithm for training algorithm has been used. The tuberculosis dataset was taken from a state hospital’s database, based on patient’s epicrisis reports.

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

Similar content being viewed by others

References

  1. Wikipedia, http://en.wikipedia.org/wiki/Mycobacterium_tuberculosis. Last accessed: 23 May, 2009.

  2. Enarson, D. A., Rieder, H. L., Arnadottir, T., and Trébucq, A., A guide for low income countries. Paris: International Union Against Tuberculosis and Lung Disease, ISBN: 2-914365-00-4, 2000.

  3. Royal College of Physicians of London, Tuberculosis. London: Royal College of Physicians of London, ISBN: 1 86016 277 0, 2006.

  4. World Health Organization, Global tuberculosis control 2008 surveillance planning financing. ISBN: 978 92 4 156354 3, Geneva, 2008.

  5. Kayaer, K., and Yıldırım, T., Medical diagnosis on Pima Indian diabetes using general regression neural networks. In Proc. of International Conference on Artificial Neural Networks and Neural Information Processing (ICANN/ICONIP): Istanbul, (pp. 181–184), 2003.

  6. Delen, D., Walker, G., and Kadam, A., Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34:2113–2127, 2005.

    Article  Google Scholar 

  7. Temurtas, F., A comparative study on thyroid disease diagnosis using neural networks. Expert Syst. Appl. 36:944–949, 2009. doi:10.1016/j.eswa.2007.10.010.

    Article  Google Scholar 

  8. Er, O., and Temurtas, F., A study on chronic obstructive pulmonary disease diagnosis using multilayer neural networks. J. Med. Syst. 32:5429–5432, 2008. doi:10.1007/s10916-008-9148-6.

    Article  Google Scholar 

  9. Rumelhart, D. E., Hinton, G. E., and Williams, R. J., Learning internal representations by error propagation. In: Rumelhart, D. E., and McClelland, J. L. (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1. Cambridge: MIT Press, pp. 318–362, 1986.

    Google Scholar 

  10. El-Solh, A. A., Hsiao, C.-B., Goodnough, S., Serghani, J., and Grant, B. J. B., Predicting active pulmonary tuberculosis using an artificial neural network. Chest. 116:968–973, 1999. doi:10.1378/ chest.116.4.968.

    Article  Google Scholar 

  11. dos Santos, A. M., Pereira, B. B., and de Seixas, J. M., Neural networks: an application for predicting smear negative pulmonary tuberculosis. Proceedings of the Statistics in the Health Sciences, March, 2004.

  12. Er, O., Temurtas, F., and Tanrıkulu, A. Ç., Tuberculosis disease diagnosis using artificial neural networks. J. Med. Syst., 2008. doi:10.1007/s10916-008-9241-x.

    Google Scholar 

  13. Montana, D. J., and Davis, L., Training feedforward neural networks using genetic algorithms. Cambridge: BBN Systems and Technologies Corp., 1989.

  14. Goldberg, D. E., Genetic algorithms in search, optimization, and machine learning. London, Addison-Wesley, 1989.

  15. Černohorský, D., Raida, Z., Škvor, Z., and Nováček, Z., Analýza a optimalizace mikrovlnných struktur. (Analysis and optimization of the microwave tructures). VUTIUM: Brno, 1999. ISBN 80-214-1512-6.

    Google Scholar 

  16. Demuth, H., and Beale, M., Neural network toolbox for use with MATLAB. User’s Guide (nnet.pdf in MATLAB help), 1993.

  17. Raida, Z., Modeling EM structures in the neural network toolbox of MATLAB. IEEE Antennas & Propagation Magazine. 44 (6)46–67, 2002. ISSN 1045-9243.

    Article  Google Scholar 

  18. Gupta, M., M., Jin, L., and Homma, N., Static and dynamic neural networks from fundamentals. New York: Wiley, 2003.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Sakarya University, 54187, Adapazari, Turkey

    Erhan Elveren & Nejat Yumuşak

Authors
  1. Erhan Elveren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nejat Yumuşak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erhan Elveren.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Elveren, E., Yumuşak, N. Tuberculosis Disease Diagnosis Using Artificial Neural Network Trained with Genetic Algorithm. J Med Syst 35, 329–332 (2011). https://doi.org/10.1007/s10916-009-9369-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-009-9369-3

Keywords

Search

Navigation