CFP last date
20 December 2024
Reseach Article

Disease Diagnosis using Soft Computing Model: A Digest

by Mohammed Abdullah Alghamdi, Sunil G. Bhirud, M. Afshar Alam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 10
Year of Publication: 2014
Authors: Mohammed Abdullah Alghamdi, Sunil G. Bhirud, M. Afshar Alam
10.5120/17855-8828

Mohammed Abdullah Alghamdi, Sunil G. Bhirud, M. Afshar Alam . Disease Diagnosis using Soft Computing Model: A Digest. International Journal of Computer Applications. 102, 10 ( September 2014), 43-47. DOI=10.5120/17855-8828

@article{ 10.5120/17855-8828,
author = { Mohammed Abdullah Alghamdi, Sunil G. Bhirud, M. Afshar Alam },
title = { Disease Diagnosis using Soft Computing Model: A Digest },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 10 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 43-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number10/17855-8828/ },
doi = { 10.5120/17855-8828 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:32:48.114241+05:30
%A Mohammed Abdullah Alghamdi
%A Sunil G. Bhirud
%A M. Afshar Alam
%T Disease Diagnosis using Soft Computing Model: A Digest
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 10
%P 43-47
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In today's era, medical diagnosis has become one of the most progressive disciplines. Since people are taking hybrid food in the daily life, different disease came into existence. It made people very careful for their health. In some cases, lack of knowledge of disease in doctor causes the patient death. Therefore, we require such diagnosis system for non-expertise so that right prescription can come for the patient. Over the years, soft computing plays the major role for computer aided disease diagnosis in physician decision process. Considering these issues, a lot work has been devoted to this discipline. Several disease diagnosis systems based on fuzzy, rule based reasoning, case based reasoning etc. have been proposed for different disease using their symptoms. Hepatitis diagnosis system is good example which is based on CBR. In this paper, we reviewed such types of diagnosis system and their techniques used. We also emphasizes on the liver disease diagnosis system. Moreover, we also found shortcoming in the present systems that cause the vague diagnosis system. Hence, patient gets more illness or sometimes costs as death. We also directed future work that will help to make the present system more robust, reliable, and helpful for non-expertise.

References
  1. Manuel Cruz-Ramírez , Cesar Hervas-Martínez, Juan Carlos Fernandez, Javier Brice no, and Manuel de la Mata " Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks," Artificial Intelligence in Medicine, vol. 58 , pp. 37-49, 2013.
  2. Chun-Ling Chuang, "case-based reasoning support for liver disease diagnosis," Artificial Intelligence in Medicine, vol. 53, pp. 15-23, 2001.
  3. Rong-Ho Lin, "An intelligent model for liver disease diagnosis" Artificial Intelligence in Medicine, Vol. 47, No. 1, pp. 53–62, September 2009.
  4. Ihsan Omur Bucak and Semra Baki, "Diagnosis of liver disease by using CMAC neural network approach," Expert Systems with Applications, vol. 37, pp. 6157-6164, 2010.
  5. Rong-Ho Lin and Chun-LingChuang, "A hybrid diagnosis model for determining the types of the liver disease," Computers in Biologyand Medicine, vol. 40, PP. 665–670, 2010.
  6. Anil Arora,Praveen Sharma,"Non-invasive Diagnosis of Fibrosis in Non-alcoholic Fatty Liver Disease,"Journal of Clinical & Experimental Hepatology,Vol. 2, No. 2 , pp. 145-155, June 2012.
  7. S. Karthik, A. Priyadarishini, J. Anuradha and B. K. Tripathy " Classification and Rule Extraction using Rough Set for Diagnosis of Liver Disease and its Types, " Pelagia Research Library Advances in Applied Science Research, Vol. 2, NO. 3, pp. 334-345, 2011.
  8. Hui-Ling Chen, Da-You Liu , Bo Yang, Jie Liu and Gang Wang " A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis gnosis ," Expert Systems with Applications, Vol. 38, pp. 11796-11803, 2011.
  9. http://archive. ics. uci. edu/ml/dataseHYPERLINK "http://archive. ics. uci. edu/ml/datasets/ILPD+%28Indian+Liver+Patient+Dataset%29"ts/ILPD+%28Indian+Liver+Patient+Dataset%29#
  10. Giveki, Davar, et al. "Automatic detection of diabetes diagnosis using feature weighted support vector machines based on mutual information and modified cuckoo search. " arXiv preprint arXiv:1201. 2173 (2012).
  11. Polat, Kemal, Salih Güne?, and Ahmet Arslan. "A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine. " Expert Systems with Applications 34. 1 (2008): 482-487.
  12. Lacagnina, Valerio, et al. "Comparison between statistical and fuzzy approaches for improving diagnostic decision making in patients with chronic nasal symptoms. " Fuzzy Sets and Systems 237 (2014): 136-150.
  13. Nielsen, Thomas Dyhre, and Finn Verner Jensen. Bayesian networks and decision graphs. Springer, 2009.
  14. Polat, Kemal, and Salih Güne?. "An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. " Digital Signal Processing 17. 4 (2007): 702-710.
  15. Giveki, Davar, et al. "Automatic detection of diabetes diagnosis using feature weighted support vector machines based on mutual information and modified cuckoo search. " arXiv preprint arXiv:1201. 2173 (2012).
  16. Temurtas, Feyzullah. "A comparative study on thyroid disease diagnosis using neural networks. " Expert Systems with Applications 36. 1 (2009): 944-949.
  17. Hoshi, Kenji, et al. "An analysis of thyroid function diagnosis using Bayesian-type and SOM-type neural networks. " Chemical and pharmaceutical bulletin53. 12 (2005): 1570-1574.
  18. Chae, Young Moon, et al. "A clinical decision support system for diagnosis of hearing loss. " Korean Journal of Preventive Medicine 22. 1 (1989): 57-64.
  19. Uzoka, Faith-Michael E. , Joseph Osuji, and Okure Obot. "Clinical decision support system (DSS) in the diagnosis of malaria: A case comparison of two soft computing methodologies. " Expert Systems with Applications 38. 3 (2011): 1537-1553.
Index Terms

Computer Science
Information Sciences

Keywords

Soft computing Disease Diagnosis neural network case based reasoning