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Review on Diagnosis of Diabetes in Pima Indians

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IJCA Proceedings on National Conference on Digital Image and Signal Processing
© 2016 by IJCA Journal
NCDISP 2016 - Number 2
Year of Publication: 2016
Authors:
Anupriya K. Kamble
Ramesh R. Manza
Yogesh M. Rajput

Anupriya K Kamble, Ramesh R Manza and Yogesh M Rajput. Article: Review on Diagnosis of Diabetes in Pima Indians. IJCA Proceedings on National Conference on Digital Image and Signal Processing NCDISP 2016(2):12-15, August 2016. Full text available. BibTeX

@article{key:article,
	author = {Anupriya K. Kamble and Ramesh R. Manza and Yogesh M. Rajput},
	title = {Article: Review on Diagnosis of Diabetes in Pima Indians},
	journal = {IJCA Proceedings on National Conference on Digital Image and Signal Processing},
	year = {2016},
	volume = {NCDISP 2016},
	number = {2},
	pages = {12-15},
	month = {August},
	note = {Full text available}
}

Abstract

Diabetes is a disorder that most of the people suffer from and which also leads to death many of the times. Worldwide people suffer from the diabetes and the number is increasing day by day. Type 1 DM, Type 2 DM and Gestational diabetes are the types of diabetes. The main cause is due to prolong existence of high blood sugar level. There are many techniques and methods by which it can be diagnosed like image processing, pattern recognition, microwave tomography and so many and so forth. The present study mainly deals with the review for diagnosing diabetes in Pima Indians by using various pattern recognition techniques. The study done so far is by using a common database for each technique. While going through the review articles it was found that each different authors have applied different techniques. Some authors introduced new techniques and displayed their results by conducting new experiments and comparing with the old techniques. In which Comparative Disease Profile (CDP) and Separability of Split Value (SSV)gave accuracy of 76. 4% and 74. 8% respectively. It was also found that in some of the papers the technique was not used only for diabetes but for other diseases or disorder too. But as our aim was to study only on diabetes the results that are given have specifically mentioned in the result column itself with diabetes word specification. Invention of the new techniques gave satisfying result to authors. The above specified results will be much useful for the future study of the current review.

References

  • "About diabetes". World Health Organization. Cited 2015 November 30.
  • "Update 2014". IDF. International Diabetes Federation. Cited 2015 November 30.
  • Shi, Yuankai; Hu, Frank B (2014 June 7). "The global implications of diabetes and cancer". The Lancet 383 (9933): 1947-8. doi:10. 1016/S0140-6736(14)60886-2. PMID 24910221.
  • Vos T, Flaxman AD, Naghavi M, Lozano R, Michaud C, Ezzati M, Shibuya K, Salomon JA, Abdalla S, Aboyans V; et al. (2012 Dec 15). "Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. ". Lancet 380 (9859): 2163-96 doi:10. 1016/S01460-6736(12) 61729-2. PMID 23245607
  • Shoback 2011. Greenspan's basic & clinical endocrinology. Edited by David G. Gardner, Dolores(9th ed. ). New York:McGraw-Hill Medical.
  • National Diabetes Clearinghouse (NDIC):National Diabetes Statistics 2011". U. S. Department of Health and Human Services. Cited 2015 Dec. 1.
  • "Diabetes Fact Sheet No312". WHO. October 2013. Cited 2015 Dec. 1.
  • Ripoll, Brian C. Leutholz, Ignacio, 2011 April 25. Exercise and disease management [2nd edition]. Boca Raton : CRC Press. p. 25. ISBN 978-1–4398–2759–8.
  • Editor, Leonid Poretsky, 2009. Principles of diabetes mellitus (2nd ed. ). New York : Springer. p. 3. ISBN 978-0-387-09840-1.
  • Murphy PM, Aha KW, 1994. UCI Repository of machine learning databases, http://www. ics. uci. edu/ ~mlearn/MLRepository. html], Irvine, CA: University of California, Department of Information and Computer Science.
  • Peter Sykacek and Stephen J. Roberts, 2002. Adaptive Classification by Variational Kalman Filtering. NIPS.
  • Liping Wei and Russ B. Altman. An Automated System for Generating Comparative Disease Profiles and Making Diagnoses. Section on Medical Informatics Stanford University School of Medicine, MSOB X215.
  • Kristin P. Bennett and Erin J. Bredensteiner, 9. 1997. A Parametric Optimization Method for Machine Learning. INFORMS Journal on Computing.
  • Andrew Watkins and Jon Timmis and Lois C. Boggess. Artificial Immune Recognition System (AIRS): An ImmuneInspired Supervised Learning Algorithm. (abw5,jt6@kent. ac. uk) Computing Laboratory, University of Kent.
  • Stefan R uping. A Simple Method For Estimating Conditional Probabilities For SVMs. CS Department, AI Unit Dortmund University.
  • Marina Skurichina and Robert P W Duin, 2000. Boosting in Linear Discriminant Analysis. Multiple Classifier Systems.
  • Lawrence O. Hall and Nitesh V. Chawla and Kevin W. Bowyer. Combining Decision Trees Learned in Parallel. Department of Computer Science and Engineering, ENB 118 University of South Florida.
  • Peter D. Turney, 1995. Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm. CoRR, csAI/9503102.
  • Ilya Blayvas and Ron Kimmel. Efficient Classification via Multiresolution Training Set Approximation. CS Dept. Technion.
  • Kristin P. Bennett and Ayhan Demiriz and Richard Maclin, 2002. Exploiting unlabeled data in ensemble methods. KDD.
  • Jennifer A. Blue and Kristin P. Bennett, 1996. Hybrid Extreme Point Tabu Search. Department of Mathematical Sciences Rensselaer Polytechnic Institute.
  • Ahmed Hussain Khan and Intensive Care. Multiplier-Free Feedforward Networks. 174.
  • Michael Lindenbaum and Shaul Markovitch and Dmitry Rusakov. Selective Sampling Using Random Field Modelling.
  • Krzysztof Grabczewski and Wl/odzisl/aw Duch. THE SEPARABILITY OF SPLIT VALUE CRITERION. Department of Computer Methods, Nicolaus Copernicus University.