CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Web-based Fuzzy Expert System for Symptomatic Risk Assessment of Diabetes Mellitus

by I. K. Mujawar, B. T. Jadhav, Kapil Patil
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 3
Year of Publication: 2018
Authors: I. K. Mujawar, B. T. Jadhav, Kapil Patil
10.5120/ijca2018917482

I. K. Mujawar, B. T. Jadhav, Kapil Patil . Web-based Fuzzy Expert System for Symptomatic Risk Assessment of Diabetes Mellitus. International Journal of Computer Applications. 182, 3 ( Jul 2018), 5-12. DOI=10.5120/ijca2018917482

@article{ 10.5120/ijca2018917482,
author = { I. K. Mujawar, B. T. Jadhav, Kapil Patil },
title = { Web-based Fuzzy Expert System for Symptomatic Risk Assessment of Diabetes Mellitus },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2018 },
volume = { 182 },
number = { 3 },
month = { Jul },
year = { 2018 },
issn = { 0975-8887 },
pages = { 5-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number3/29740-2018917482/ },
doi = { 10.5120/ijca2018917482 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:10:15.812614+05:30
%A I. K. Mujawar
%A B. T. Jadhav
%A Kapil Patil
%T Web-based Fuzzy Expert System for Symptomatic Risk Assessment of Diabetes Mellitus
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 3
%P 5-12
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Web applications have demonstrated their assistance as helping tools for therapeutic specialists, experts and patients as well. The uses of the Internet-based innovations and the ideas of fuzzy expert system (FES) have made new strategies for sharing and circulating information. This study intends to build rule based online fuzzy expert system which assist people around the globe during the time spent management of diabetes mellitus. The proposed work presents web based expert system (Web-FESSRADM) for individuals who can check their diabetes risk and for doctors, practitioners to assess diabetes risk online. In the Web-FESSRADM development fuzzy logic approach is utilized to determine the risk of diabetes. Open source software development environment is used to develop and actualize proposed work.

References
  1. American Diabetes Association. "Diagnosis and classification of diabetes mellitus." Diabetes care 33.Suppl 1 (2010): S62.
  2. Samuel, Oluwarotimi Williams, M. O. Omisore, and B. A. Ojokoh. "A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever." Expert Systems with Applications 40.10 (2013): 4164-4171.
  3. Li, Daoliang, Zetian Fu, and Yanqing Duan. "Fish-Expert: a web-based expert system for fish disease diagnosis." Expert systems with Applications 23.3 (2002): 311-320.
  4. Dokas, Ioannis M. "Developing Web Sites For Web Based Expert Systems: A Web Engineering Approach." ITEE. 2005.
  5. Malmir, Behnam, Mohammadhossein Amini, and Shing I. Chang. "A medical decision support system for disease
  6. diagnosis under uncertainty." Expert Systems with Applications 88 (2017): 95-108.
  7. Lee, Kwang Hyung. First course on fuzzy theory and applications. Vol. 27. Springer Science & Business Media, 2006.
  8. Huang, Mu-Jung, and Mu-Yen Chen. "Integrated design of the intelligent web-based Chinese Medical Diagnostic System (CMDS)–Systematic development for digestive health." Expert Systems with Applications 32.2 (2007): 658-673.
  9. Phuong, Nguyen Hoang, and Vladik Kreinovich. "Fuzzy logic and its applications in medicine." International journal of medical informatics 62.2-3 (2001): 165-173.
  10. Djam, X. Y., et al. "A fuzzy expert system for the management of malaria." (2011).
  11. Akinyokun, Oluwole Charles, et al. "Fuzzy logic-driven expert system for the diagnosis of heart failure disease." Artificial Intelligence Research 4.1 (2014): 12.
  12. Hasan, Mir Anamul, and Ahsan Raja Chowdhury. "Human disease diagnosis using a fuzzy expert system." arXiv preprint arXiv:1006.4544 (2010).
  13. Tripathi, Brajendra Kumar, and Arvind Kumar Srivastava. "Diabetes mellitus: Complications and therapeutics." Medical science monitor 12.7 (2006): RA130-RA147.
  14. Duan, Yanqing, John S. Edwards, and M. X. Xu. "Web-based expert systems: benefits and challenges." Information & Management 42.6 (2005): 799-811.
  15. Power, Daniel J. "Web-based and model-driven decision support systems: concepts and issues." AMCIS 2000 Proceedings (2000): 387.
  16. Grove, Ralph. "Internet‐based expert systems." Expert systems 17.3 (2000): 129-135.
  17. Mujawar, Irfan & T Jadhav, B. (2017). COMPREHENSIVE STUDY ON WEB BASED EXPERT SYSTEMS FOR DISEASE DIAGNOSIS AND TREATMENT. International Journal of Computer Engineering and Applications. XI.
  18. Baghel, M. K., & Mehta, M. N. A Web Based Fuzzy Expert System for Human Disease Diagnosis. International Journal of Engineering And Computer Science ISSN, 2319-7242.
  19. Wang, Li-Xin. A course in fuzzy systems. Prentice-Hall press, USA, 1999.
  20. Tsoukalas, Lefteri H., and Robert E. Uhrig. Fuzzy and neural approaches in engineering. John Wiley & Sons, Inc., 1996.
  21. Siler, William, and James J. Buckley. Fuzzy expert systems and fuzzy reasoning. John Wiley & Sons, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Diabetes T1DM T2DM Fuzzy Logic Web Expert System Rule based Fuzzy System.

Powered by PhDFocusTM