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Disease Inference from Health-Related Questions via Fuzzy Expert System

  • Ajay P. Chainani
  • Santosh S. Chikne
  • Nikunj D. Doshi
  • Asim Z. Karel
  • Shanthi S. Therese
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 10)

Abstract

Automatic disease inference is vital to shorten the gap for patients seeking online remedies for health-related issues. Some of the persistent problems in offline medium are hectic schedules of doctors engrossed in their workload which abstain them to supervise on all health-related aspects of patients seeking advice and also community-based services which may be trivial in nature because of factors such as vocabulary gaps, incomplete information, and lack of available preprocessed samples limiting disease inference. Thus, we motivate users with proposed expert system by answering the underlying challenges. It is an iterative process working on multiple symptoms and compiles overall symptoms and causes required for inference of diseases. First, symptoms are mapped from extracted raw features. Second, fuzzy inference is made from weight-based training and catalyst factors. Thus, fuzzy-based expert systems will be boon for online health patrons who seek health-related and diagnosing information.

Keywords

Fuzzy set theory Fuzzy logic Fuzzy expert system Symptoms Diseases Disease inference Data mining Catalyst Clustering 

References

  1. 1.
    Shi, Y. and Eberhart, R. and Chen, Y., (1999) “Implementation of evolutionary fuzzy systems”, IEEE Transactions on Fuzzy Systems, Vol. 7, No. 2, pp 109–11. Google Scholar
  2. 2.
    “Human Disease Diagnosis Using a Fuzzy Expert System” Journal of Computing, Volume 2, Issue 6, June 2010, ISSN 2151-9617.Google Scholar
  3. 3.
    Symptoms of different diseases available on: http://www.webmd.com/
  4. 4.
    Symptoms of different diseases available on: http://www.mediplus.com/
  5. 5.
    Zadeh, LA, (1983) “The role of fuzzy logic in the management of uncertainty in expert systems”, Fuzzy sets and Systems On Elsevier.,Vol. 11, No. 1–3, pp 197–198.Google Scholar
  6. 6.
    Timothy J. Ross “Fuzzy Logic With Engineering Applications” Wiley.Google Scholar
  7. 7.
    J.-S. R. Jang “Neuro-Fuzzy and Soft Computing” PHI 2003.Google Scholar
  8. 8.
    M. Negnevitsky, (2005) “Artificial intelligence: A guide to intelligent systems”, Addison Wesley Longman.Google Scholar
  9. 9.
    Jacek M. Zuarada―Introduction to Artificial Neural System ‖; West Publishing Company. 1992.Google Scholar
  10. 10.
    S. Rajasekaran and G. A. Vijayalakshmi Pai “Neural Networks, Fuzzy Logic and Genetic Algorithms” PHI Learning.Google Scholar
  11. 11.
    A. Sudha, P. Gayathri, N. Jaisankar―Utilization of Data mining Approaches for Prediction of Life Threatening Diseases Survivability ‖; International Journal of Computer Applications (0975 – 8887) Volume 41– No. 17, March 2012.Google Scholar
  12. 12.
    Carlos Ordonez,―Comparing association rules and decision trees for disease prediction ‖; ACM 2006.Google Scholar
  13. 13.
    S. N. Sivanandam, S. N. Deepa “Principles of Soft Computing” Second Edition, Wiley Publication.Google Scholar
  14. 14.
    Han, J., Kamber, M,―Data Mining Concepts and Techniques‖; Morgan Kaufmann Publishers, 2006 (Second Edition).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Ajay P. Chainani
    • 1
  • Santosh S. Chikne
    • 1
  • Nikunj D. Doshi
    • 1
  • Asim Z. Karel
    • 1
  • Shanthi S. Therese
    • 1
  1. 1.Thadomal Shahani Engineering CollegeMumbaiIndia

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