[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Diagnosis of Renal Failure Disease Using Adaptive Neuro-Fuzzy Inference System

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

Abstract

Adaptive Neuro-Fuzzy Inference System (ANFIS) is one of the useful and powerful neural network approaches for the solution of function approximation and pattern recognition problems in the last decades. In this paper, the diagnosis of renal failure disease is investigated using ANFIS approach. Totally the raw data of 112 patients is obtained from Istanbul and Cerrahpasa Medical Faculties of Istanbul University, Turkey. Sixty-four of them are related to renal failures and the rest data belong to healthy persons. In ANFIS model, three rules and Gaussian membership functions are chosen, where rules are determined by the subtractive clustering method. Seven parameters of the patients are considered for the input of the system. These are: Blood Urea Nitrogen (BUN), Creatinine, Uric Acid, Potassium (K), Calcium (Ca), Phosphorus (P) and age. We try to decide whether the patient is ill or not. We have reached 100% success in ANFIS and have better results compared to Support Vector Machine (SVM) and Artificial Neural Networks (ANN).

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Beers, M. H., Berkow, R.: The Merck manual of diagnosis and therapy, chapter 222—renal failure, 17th edition. Merck Research Laboratories, Division of Merck & Co., Inc., Whitehouse Station, NJ, 1999.

    Google Scholar 

  2. Savica, V., Cala, L. A., Davis, P. A., Bellinghieri, G.: Hyperphoshoremia in kidney failure—salivary phosphate as a marker and possible target.. Clin. Nephrol. 69(3):229, 2008.

    Google Scholar 

  3. Ruilope, L. M., Garcia-Puig, J.: Hyperuricemia and renal function. Curr. Hypertens. Rep. 3(3):197-202, 2001. doi:10.1007/s11906-001-0038-2.

    Article  Google Scholar 

  4. Iseki, K., Ikemiya, Y., Inoue, T., Iseki, C., Kinjo, K., Takishita, S., Am, J.: Significance of hyperuricemia as a risk factor for developing ESRD in a screened cohort. Kidney Dis. 44(4):642-650, 2004. doi:10.1016/S0272-6386(04)00934-5.

    Google Scholar 

  5. Médaille, C., Trumel, C., Concordet, D., Vergez, F., and Braun, J. P., Comparison of plasma/serum urea creatinine concentrations in the dog: a 5 year retrospective study in a commercial veterinary clinical pathology laboratory. J. Vet. Med. A Physiol. Pathol. Clin. Med. 51(3):119–123, 2004.

    Google Scholar 

  6. Prevat, A., Martini, S., Guiqnard, J. P.: Glomerular filtration markers in pediatrics. Rev. Med. Suisse Romande. 122(12):625-630, 2002.

    Google Scholar 

  7. Jang, J.-S. R.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3):665-685, 1993. doi:10.1109/21.256541.

    Article  MathSciNet  Google Scholar 

  8. Ubeyli, E. D., Güler, I.: Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems. Comput. Biol. Med. 35(5):421-433, 2005. doi:10.1016/j.compbiomed.2004.03.003.

    Article  Google Scholar 

  9. Castellano, G., Fanelli, A. M., Mencar, C.: A neuro-fuzzy network to generate human understandable knowledge from data. Cognit. Syst. Res. 3:125-144, 2001. doi:10.1016/S1389-0417(01)00055-9.

    Article  Google Scholar 

  10. Akgundogdu, A., Gozutok, A., Kilic, N., Osman, O. N.: diagnosis of power transformer using neuro-fuzzy model. IU-JEEE. 8(2):699-706, 2008.

    Google Scholar 

  11. Jang, J.-S. R.: Self-learning fuzzy controllers based on temporal back propagation. IEEE Trans. Neural Netw. 3(5):714-723, 1992. doi:10.1109/72.159060.

    Article  Google Scholar 

  12. Sugeno, M., Kang, G. T.: Structure identification of fuzzy model. Fuzzy Sets Syst. 28:15-33, 1988. doi:10.1016/0165-0114(88)90113-3.

    Article  MATH  MathSciNet  Google Scholar 

  13. Paplinski, A. P.: Neuro-fuzzy computing. Available at: http://www.csse.monash.edu.au/courseware/cse5301/04/index.html.

  14. Chiu, S.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2(3):267-278, 1994.

    Google Scholar 

  15. Chen, J. Y., Zheng, Q., Ji, J.: A PSO-based subtractive clustering technique for designing RBF neural networks. Proceeding of IEEE World Congress on Computational Intelligence, 2047–2052, 2008.

  16. Toosi, A. N., Kahani, M., Neuro-Fuzzy, A.: Classifier for intrusion detection systems. Proceedings of the CSICC Conference, Tehran, IRAN, 2006.

Download references

Author information

Authors and Affiliations

  1. Electrical–Electronics Department, Engineering Faculty, Istanbul University, Avcilar, Istanbul, Turkey

    Abdurrahim Akgundogdu, Niyazi Kilic & Osman N. Ucan

  2. Department of Industrial Electronics, Vocational School, Yildiz Technical University, Istanbul, Turkey

    Serkan Kurt

  3. Cerrahpasa Faculty of Medicine, Istanbul University, Istanbul, Turkey

    Nilgun Akalin

Authors
  1. Abdurrahim Akgundogdu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Serkan Kurt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Niyazi Kilic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Osman N. Ucan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nilgun Akalin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdurrahim Akgundogdu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Akgundogdu, A., Kurt, S., Kilic, N. et al. Diagnosis of Renal Failure Disease Using Adaptive Neuro-Fuzzy Inference System. J Med Syst 34, 1003–1009 (2010). https://doi.org/10.1007/s10916-009-9317-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-009-9317-2

Keywords

Search

Navigation