Journal of Medical Systems

, Volume 34, Issue 3, pp 299–302 | Cite as

Tuberculosis Disease Diagnosis Using Artificial Neural Networks

  • Orhan Er
  • Feyzullah Temurtas
  • A. Çetin Tanrıkulu
Original Paper


Tuberculosis is an infectious disease, caused in most cases by microorganisms called Mycobacterium tuberculosis. Tuberculosis is a great problem in most low income countries; it is the single most frequent cause of death in individuals aged fifteen to forty-nine years. Tuberculosis is important health problem in Turkey also. In this study, a study on tuberculosis diagnosis was realized by using multilayer neural networks (MLNN). For this purpose, two different MLNN structures were used. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. A general regression neural network (GRNN) was also performed to realize tuberculosis diagnosis for the comparison. Levenberg-Marquardt algorithms were used for the training of the multilayer neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on tuberculosis diseases diagnosis. The tuberculosis dataset were taken from a state hospital’s database using patient’s epicrisis reports.


Tuberculosis disease diagnosis Multilayer neural network General regression neural network 


  1. 1.
    Enarson, D. A., Rieder, H. L., Arnadottir, T., Trébucq, A., “A Guide For Low Income Countries”, International Union Against Tuberculosis and Lung Disease, 68 boulevard Saint-Michel, 75006 Paris, France, ISBN: 2-914365-00-4, 2000.Google Scholar
  2. 2.
    Royal College of Physicians of London: “Tuberculosis”, 11 St. Andrews Place, London NW1 4LE, ISBN : 1 86016 277 0, 2006.Google Scholar
  3. 3.
    World Health Organization: “Global Tuberculosis Control 2008 Surveillance Planning Financing”, ISBN : 978 92 4 156354 3, Geneva, 2008.Google Scholar
  4. 4.
    Kayaer, K., 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.Google Scholar
  5. 5.
    Delen, D., Walker, G., and Kadam, A., Predicting breast cancer survivability: A comparison of three data mining methods. Artif. Intell. Med. 34:2113–127, 2005.CrossRefGoogle Scholar
  6. 6.
    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.CrossRefGoogle Scholar
  7. 7.
    Er, O., and Temurtas, F., A Study on Chronic Obstructive Pulmonary Disease Diagnosis Using Multilayer Neural Networks. J. Med. Syst. 32:5429–432, 2008. doi: 10.1007/s10916-008-9148-6.CrossRefGoogle Scholar
  8. 8.
    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 CognitionMIT Press, Vol. 1. Cambridge, MA, pp. 318–362, 1986.Google Scholar
  9. 9.
    Brent, R. P., Fast training algorithms for multi-layer neural nets. IEEE Trans. Neural Netw. 2:346–354, 1991. doi: 10.1109/72.97911.CrossRefGoogle Scholar
  10. 10.
    Gori, M., and Tesi, A., On the problem of local minima in backpropagation. IEEE Trans. Pattern Anal. Mach. Intell. 14:76–85, 1992. doi: 10.1109/34.107014.CrossRefGoogle Scholar
  11. 11.
    Hagan, M. T., and Menhaj, M., Training feed forward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5:989–993, 1994. doi: 10.1109/72.329697.CrossRefGoogle Scholar
  12. 12.
    Hagan, M. T., Demuth, H. B., and Beale, M. H., Neural Network Design. PWS Publishing, Boston, MA, 1996.Google Scholar
  13. 13.
    Gulbag, A., and Temurtas, F., A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro fuzzy inference systems. Sens. Actuators B Chem. 115:252–262, 2006. doi: 10.1016/j.snb.2005.09.009.CrossRefGoogle Scholar
  14. 14.
    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.CrossRefGoogle Scholar
  15. 15.
    dos Santos, A. M., Pereira, B. B., de Seixas, J. M., “Neural Networks: An Application for Predicting Smear Negative Pulmonary Tuberculosis”, Proceedings of the Statistics in the Health Sciences, March, 2004.Google Scholar
  16. 16.
    Matlab® Documentation., Version 7.0, Release 14, The MathWorks, Inc, 2004.Google Scholar
  17. 17.
    Ozyılmaz, L., and Yıldırım, T., Diagnosis of thyroid disease using artificial neural network methods In Proc. of ICONIP’02 9th international conference on neural information processing. Orchid Country Club, Singapore, pp. 2033–2036, 2002.Google Scholar
  18. 18.
    Watkins, A., “AIRS: A resource limited artificial immune classifier”. Master Thesis, Mississippi State University, 2001.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Orhan Er
    • 1
  • Feyzullah Temurtas
    • 2
  • A. Çetin Tanrıkulu
    • 3
  1. 1.Department of Electrical and Electronics EngineeringSakarya UniversityAdapazariTurkey
  2. 2.Department of Electrical and Electronics EngineeringBozok UniversityYozgatTurkey
  3. 3.Department of Chest DiseasesSutcu Imam UniversityKahramanmarasTurkey

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