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Soft Computing Diagnostic System for Diabetes

by Pankaj Srivastava, Neeraja Sharma, Richa Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 18
Year of Publication: 2012
Authors: Pankaj Srivastava, Neeraja Sharma, Richa Singh
10.5120/7288-0407

Pankaj Srivastava, Neeraja Sharma, Richa Singh . Soft Computing Diagnostic System for Diabetes. International Journal of Computer Applications. 47, 18 ( June 2012), 22-27. DOI=10.5120/7288-0407

@article{ 10.5120/7288-0407,
author = { Pankaj Srivastava, Neeraja Sharma, Richa Singh },
title = { Soft Computing Diagnostic System for Diabetes },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 18 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number18/7288-0407/ },
doi = { 10.5120/7288-0407 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:42:11.688842+05:30
%A Pankaj Srivastava
%A Neeraja Sharma
%A Richa Singh
%T Soft Computing Diagnostic System for Diabetes
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 18
%P 22-27
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the present paper, we propose a soft computing diagnostic system for detecting different phases of diabetes. The proposed system is user friendly and will guide patients to evolve proper strategies so that they could maintain their blood sugar level by adopting suitable life style. The proposed system not only acts as a referral system in between patient and medical expert but also sharpen the diagnostic process of medical experts. A number of cases on the basis of available clinical datas have been investigated to check the validity of system.

References
  1. Baskaran, A. , Karthikeyan, D. , and Swamy, A. T. (2010). Modeling and Automation of Diagnosis and Treatment of Diabetes, Lecture Notes in Computer Science, Vol. 6457, pp. 339-348.
  2. Bellman, R. E. and Zadh, L. A. (1970). Decision Making in a Fuzzy Environment, Management Science, Vol. 17, No. 4, pp. B141-B164.
  3. Clinical Research on the Benefits of Yoga Practice on Diabetes. (2009). The Effects of Exercise and Yoga on Diabetes, Health Administrator, Vol. XXII, No. 1&2, pp. 42-45.
  4. Degani, Rosanna. (1992). Computerized Electrocardiogr- am Diagnosis: Fuzzy Approach, Methods of Information in Medicine, Vol. 31, pp. 225- 233.
  5. Jain, Ramesh. (1967). Decision making in the Presence of Fuzzy Variables, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 6, No. 10, pp. 698-703.
  6. Jena, R. K and et. al. (2004). Soft Computing Methodologies in Bioinformatics, European Journal of Scientific Research Vol. 26, pp. 183-203.
  7. Klire, George. J. and Yuan, Bo. (2009). Fuzzy Sets and Fuzzy Logic: Theory and Applications, PHI Learning Private Limited: New Delhi.
  8. Lamb, Trisha. (2006). Yoga and Diabetes, International Association of Yoga Therapists.
  9. Malhotra, V. and et al. (2004). Effect of Yoga Asans and Pranayama in Non-Insulin Dependent Diabetes Mellitus, Indian Journal of Traditional Knowledge, Vol. 3, No. 02, pp. 162-167.
  10. Pandey D. , Mahajan, Vaishali and Srivastava Pankaj (2006). Rule Based System for Cardiac Analysis, NATL ACAD SCI LETT, Vol. 29, No. 7 & 8, pp. 299-309.
  11. Polat, K. and G?ne, S. (2007). An Expert System Approach Based on Principal Component Analysis and Adaptive Neuro-Fuzzy Inference System to Diagnosis of Diabetes Disease, Digital Signal Processing, Vol. 17, No. 04, pp. 702-710.
  12. Sandeep, S. , Ganesan, A. and Mohan, V. Development and Updation of the Diabetes Atlas of India, Madras Diabetes Research Foundation, Chennai.
  13. Srivastava, Pankaj and Sharma, Neeraja. Soft Computing Criterion for ECG Beats Classification and Cardiac Analysis, Communicated.
  14. Srivastava, Pankaj and Srivastava, Amit. (2012). A note on Soft Computing Approach for Cardiac Analysis, J. Basic. Appl. Sci. Res. , Vol. 2, No. 1, pp. 376-385.
  15. Srivastava, Pankaj and Srivastava, Amit. (2012). Spectrum of Soft Computing Risk Assessment Scheme for Hypertension, International Journal of Computer Applications (0975-8887), Vol. 44, No. 17, pp. 23-30.
  16. Warren, Jim, Beliakov, Gleb and Berend van der Zwaag. (2000). Fuzzy Logic in Clinical Practice Decision Support Systems, 33rd Hawaii International Conference on System Sciences, Vol. 05, pp. 1-10.
Index Terms

Computer Science
Information Sciences

Keywords

Diabetes Soft Computing Diagnostic System Fuzzy Tools

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