Skip to main content

A Study on Hepatitis Disease Diagnosis Using Probabilistic Neural Network


Hepatitis is a major public health problem all around the world. Hepatitis disease diagnosis via proper interpretation of the hepatitis data is an important classification problem. In this study, a comparative hepatitis disease diagnosis study was realized. For this purpose, a probabilistic neural network structure was used. The results of the study were compared with the results of the previous studies reported focusing on hepatitis disease diagnosis and using same UCI machine learning database.

This is a preview of subscription content, access via your institution.

Fig. 1



  2. Specht, D. F., Probabilistic neural networks. Neural Netw. 3:109–118, 1990.

    Article  Google Scholar 

  3. Gulbag, A., Temurtas, F., and Yusubov, I., Quantitative discrimination of the binary gas mixtures using a combinational structure of the probabilistic and multilayer neural networks. Sens. Actuators B: Chem. 131:196–204, 2007. doi:10.1016/j.snb.2007.11.008.

    Article  Google Scholar 

  4. Temurtas, F., A comparative study on thyroid disease diagnosis using neural networks. Expert Syst. Appl. 36:944–949, 2009.

    Article  Google Scholar 

  5. Temurtas, H., Yumusak, N., and Temurtas, F., A comparative study on diabetes disease diagnosis using neural networks. Expert Syst. Appl. 36:8610–8615, 2009.

    Article  Google Scholar 

  6. Carpenter, G. A., and Markuzon, N., ARTMAP-IC and medical diagnosis: Instance counting and inconsistent cases. Neural Netw. 11:323–336, 1998.

    Article  Google Scholar 

  7. Deng, D., and Kasabov, N., On-line pattern analysis by evolving self-organizing maps. In Proc. of the 5th Biannual Conference on Aritificial Neural Networks and Expert Systems (ANNES), Dunedin, 2001, pp. 46–51.

  8. 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, 2003, pp. 181–184.

  9. Hoshi, K., Kawakami, J., Kumagai, M., Kasahara, S., Nisimura, N., Nakamura, H., and Sato, K., An analysis of thyroid function diagnosis using bayesian-type and SOM-type neural networks. Chem. Pharm. Bull. 53:1570–1574, 2005.

    Article  Google Scholar 

  10. Polat, K., and Güneş, S., Medical Decission support system based on artificial immune recognation system(AIRS), fuzzy weighted pre-processing and feature selection. Expert Syst. Appl 33(2007):484–490, 2007.

    Article  Google Scholar 

  11. Bascil, M. S., and Temurtas, F., A study on hepatitis disease diagnosis using multilayer neural network with Levenberg Marquardt Training Algorithm. J. Med. Syst. doi:10.1007/s10916-009-9378-2.

  12. Özyılmaz, L., and Yıldırım, T., Artificial neural networks for diagnosis of hepatitis disease, in:International Joint Conference on Neural Networks (IJCNN), Portland, OR, USA, July 20–24, vol. 1, 2003, pp. 586–589.

  13. Polat, K., and Güneş, S., Hepatitis disease diagnosis using a new hybrid system based on future selection(FS) and artificial immune recognition system with fuzzy resource allocation. Digital Signal Process. 16(2006):889–901, 2006.

    Article  Google Scholar 

  14. Polat, K., and Güneş, S., A hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRS. Comput. Methods Programs Biomed. 88(2007):164–174, 2007.

    Article  Google Scholar 

  15. Doğantekin, E., Doğantekin, A., and Avcı, D., Automatic hepatitis diagnosis system based on Linear Discriminant Analysis and AdaptiveNetwork based on Fuzzy Inference System. Expert Syst. Appl. 36(2009):11282–11286, 2009.

    Article  Google Scholar 

  16. Matlab® Documentation, Version 7.0, Release 14, The MathWorks, Inc. 2004.

  17. Watkins, A., AIRS: A resource limited artificial immune classifier. Master Thesis, Mississippi State University, 2001.

  18. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. G. R., Advances in knowledge discovery and data mining. AAAI Press/The MIT Press, Menlo Park, 1996.

    Google Scholar 

  19. Delen, D., Walker, G., and Kadam, A., Predicting breast cancer survivability: A comparison of three data mining methods. Artif. Intell. Med. 34(2):113–127, 2005.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Halit Oztekin.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Bascil, M.S., Oztekin, H. A Study on Hepatitis Disease Diagnosis Using Probabilistic Neural Network. J Med Syst 36, 1603–1606 (2012).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Hepatitis disease diagnosis
  • Probabilistic neural network