Neural Network Based Rule Extraction for Analysing Risk Factors and Stages in Cervical Cancer—An Analytical Study

  • D. Sowjanya Latha
  • P.V. Lakshmi
  • Sameen Fatima
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


The high performance and modelling ability of a Neural Network has enabled it to be used extensively in most domains, specifically in medical domain. Inspite of its excellent modelling performance, NN acts as a black-box because of its inability to provide a simple interpretation of the model. Analysing the network model is a challenging task. Several studies have been carried in this direction. In this paper we would like to propose a method for extracting knowledge from the model obtained by a multi layer perceptron. The rules extracted are precise and accurate. This method has been applied on cervical cancer data for its performance and found to produce accurate results. This technique is simple, efficient and comparable to the traditional rule based algorithms like J48. The results would be most valuable to the medical practitioners in diagnosing the disease at a very early stage.


Neural network Rule extraction MLP 


  1. 1.
    Salama AS, Elabarby OG (2012) Fuzzy rough set and fuzzy ID3decision approaches to knowledge discovery in datasets. ISPACS 2012:25Google Scholar
  2. 2.
    Nester Jeyakumar M et al (2012) Improved Classifier performance through genetic algorithm for cervical cancer prediction. J Res BioinformGoogle Scholar
  3. 3.
    Craven MW, Shavlik JW (1996) Extracting tree structured representation from trained networks. In: Advances in neural information processing systems, vol 8. MIT Press, CambridgeGoogle Scholar
  4. 4.
    Muslimi B et al (2008) An efficient technique for extracting fuzzy rules from neural networks. World Acad Sci Eng Technol 16:296–302Google Scholar
  5. 5.
    Shavlik JW, Mooney RJ, Towell GG (1991) Symbolic and neural learning algorithms: an experimental comparison. Mach Learn 6(2):111–143Google Scholar
  6. 6.
    Towell G, Shavlik JW (1993) The extraction of refined rules from knowledge-based neural networks. Mach Learn 131:71–101Google Scholar
  7. 7.
    Setiono R, Leow WK, Zurada JM (2002) Extraction of rules from artificial neural networks for nonlinear regression. IEEE Trans Neural Netw 13:3Google Scholar
  8. 8.
    Tickle AB, Orlowski M, Diederich J (1994) DEDEC: decision detection by rule extraction from neural network. QUT NRCGoogle Scholar
  9. 9.
    Huang S, Xing H (2001) Extracting intelligible and concise fuzzy rules from neural networks. Fuzzy Sets Syst 132:233–243MathSciNetCrossRefGoogle Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  • D. Sowjanya Latha
    • 1
  • P.V. Lakshmi
    • 2
  • Sameen Fatima
    • 3
  1. 1.Department of MCAAMS School of InformaticsHyderabadIndia
  2. 2.Department of Information TechnologyGITAM UniversityVisakhapatnamIndia
  3. 3.Department of Computer ScienceOsmania UniversityHyderabadIndia

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