Computational intelligence in early diabetes diagnosis: A review

Review of Diabetic Studies

Volumn 7, Issue 4, 2010, Pages 252-261

  Shankaracharya, ^a   Odedra, Devang ^a   Samanta, Subir ^a   Vidyarthi, Ambarish S ^a  

^a BIRLA INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Algorithm; Artificial neural network; Computational; Diabetes diagnosis; Learning; Logistic regression

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; DATA ANALYSIS; DECISION MAKING; DIABETES MELLITUS; EARLY DIAGNOSIS; LOGISTIC REGRESSION ANALYSIS; MACHINE LEARNING; REVIEW; SUPPORT VECTOR MACHINE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; HUMAN;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DIABETES MELLITUS; EARLY DIAGNOSIS; HUMANS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED;

EID: 80055088256     PISSN: 16136071     EISSN: 16140575     Source Type: Journal    
DOI: 10.1900/RDS.2010.7.252     Document Type: Review

References (56)

1. Lieberman LS. Dietary, evolutionary, and modernizing influences on the prevalence of type 2 diabetes. Annu Rev Nutr 2003. 23:345-377.
2. Zhang Y, Dall T, Mann SE, Chen Y, Martin J, Moore V, Baldwin A, Reidel VA, Quick WW. The economic costs of undiagnosed diabetes. Popul Health Manag 2009. 12(2):95-101.
3. Jackson DM, Wills R, Davies J, Meadows K, Singh BM, Wise PH. Public awareness of the symptoms of diabetes mellitus. Diabet Med 1991. 8:971-972.
4. Harris MI, Klein R, Wellborn TA, Knuiman MW. Onset of NIDDM occurs at least 4-7 yr before clinical diagnosis. Diabetes Care 1992. 15:815-819.
5. Knowler WC, Barrett-Connor E, Fowler SE, Ham-man RF, Lachin JM, Walker EA, Nathan DM. Reduction in the incidence of type 2 diabetes with lifestyle inter vention or metformin. N Engl J Med 2002. 346(6):393-403.
6. Paulweber B, Valensi P, Lindström J Lalic NM, Greaves CJ, McKee M, Kissimova-Skarbek K, Liatis S, Cosson E, Szendroedi J, et al. A European evidence-based guideline for the prevention of type 2 diabetes. Horm Metab Res 2010. 42(Suppl 1):S3-S36.
7. Lindstrom J, Neumann A, Sheppard KE, Gilis-Januszewska A, Greaves CJ, Handke U, Pajunen P, Puhl S, Pölönen A, Rissanen A, et al. Take action to prevent diabetes the IMAGE toolkit for the prevention of type 2 diabetes in Europe. Horm Metab Res 2010. 42 (Suppl 1):S37-S55.
8. Schwarz PE, Li J, Lindstorm J, Tuomilehto J. Tools for predicting the risk of type 2 diabetes in daily practice. Horm Metab Res 2009. 41(2):86-97.
9. Sajda P. Machine learning for detection and diagnosis of disease. Annu Rev Biomed Eng 2006. 8:537-565.
10. Frank A, Asuncion A. UCI machine learning repository. Irvine, CA, University of California, School of Information and Computer Science, 2010.
11. Kazemnejad A, Batvandi Z, Faradmal J. Comparison of artificial neural network and binary logistic regression for determination of impaired glucose tolerance/diabetes. East Mediterr Health J 2010. 16(6):615-620.
12. Dey R, Bajpai V, Gandhi G, et al. Application of artificial neural network technique for diagnosing diabetes mellitus. IEEE Third International Conference on Industrial and Information Systems, Kharagpur, India, 2008, 1-4.
13. Heikes KE, Eddy DM, Arondekar B, Schlessinger L. Diabetes risk calculator: a simple tool for detecting undiagnosed diabetes and pre-diabetes. Diabetes Care 2008. 31:1040-1045.
14. Abbot RD. Logistic regression in survival analysis. Am J Epidemiol 1985. 121(3):465-471.
15. Shanker MS. Using neural networks to predict the onset of diabetes mellitus. J Chem Inf Comput Sci 1996. 36:35-41.
16. Kordos M, Blachnik M, Strzempa D. Do we need whatever more than k-NN? In: Proceedings of the 10th International Conference on Artificial Intelligence and Soft Computing, Part I, Springer-Verlag Berlin, 2010, 414-421.
17. Ster B, Dobnikar A. Neural networks in medical diagnosis: comparison with other methods. In: Proceedings of the International Conference on Engineering Applications with Neural Networks, London, 1996, 427-430.
18. Patil BM, Joshi RC, Toshniwal D. Hybrid prediction model for type-2 diabetic patients. Exp Syst Appl 2010. 37:8102-8108.
19. Jantan H, Hamdan AR, Othman ZA. Human talent prediction in HRM using C4.5 classification algorithm. Int J Comp Sci Engin 2010. 2:2526-2534.
20. Kohonen T. Self-organizing formation of topologically correct feature maps. Biol Cybern 1982. 43:59-69.
21. Fritzke B. A growing neural gas network learns topologies. Adv Neural Inf Process Syst 1995. 7:625-632.
22. Fritzke B. Growing cell structures a self-organizing network for unsupervised and supervised learning. Neural Netw 1994. 7:1441-1460.
23. Deng D, Kasabov N. On-line pattern analysis by evolving self-organizing maps. Proceedings of the 5th Biannual Conference on Aritificial Neural Networks and Expert Systems (ANNES), Dunedin, 2001, 46-51.
24. Ali S, Abraham A. An empirical comparison of kernel selection for support vector machines. 2nd International Conference on Hybrid Intelligent Systems, Soft Computing systems: Design, Management and Applications, IOS Press, The Netherlands, 2002, 321-330.
25. Barakat NH, Bradley AP, Barakat MB. Intelligible support vector machines for diagnosis of diabetes mellitus. Trans Inf Technol Biomed 2010. 14:1114-1120.
26. Gunes PK, Aslan A. A cascade learning system for classification of diabetes disease: generalized discriminant analysis and least square support vector machine. Exp Syst Appl 2008. 34:214-221.
27. Purnami SW, Embong A, Zain JM. A New smooth support vector machine and its applications in diabetes disease diagnosis. J Comp Sci 2009. 5:1006-1011.
28. Lee YJ, Mangasarian OL. A smooth support vector machine. J Comp Optim Appl 2001. 20:5-22.
29. Basheer IA, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Meth 2000. 43:3-31.
30. Brent RP. Fast training algorithms for multi-layer neural nets. IEEE Trans Neural Netw 1991. 2(3):346-354.
31. Gori M, Tesi A. On the problem of local minima in backpropagation. IEEE Trans Pattern Anal Mach Intell 1992. 14:76-85.
32. Jaafar, SF, Ali DM. Diabetes mellitus forecast using artificial neural networks. Asian conference of paramedical research proceedings, Kuala Lumpur, Malaysia, 2005, 5-7
33. Eskaf EK, Badawi O, Ritchings T. Predicting blood glucose levels in diabetes using feature extraction and artificial neural networks. Third ICTTA conference, Damascus, 2008, 1-6.
34. Jayalakshmi T, Santhakumaran A. A novel classification method for classification of diabetes mellitus using artificial neural networks. International Conference on Data Storage and Data Engineering, Bangalore, 2010, 159-163.
35. Chen LH, Chang S. An adaptive learning algorithm for principal component analysis. IEEE Trans Neural Netw 1995. 6:1255-1263.
36. Hagan MT, Menhaj M. Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 1994. 5:989-993.
37. Gulbag A, Temurtas F. A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro fuzzy inference systems. Sens Actuators B Chem 2006. 115:252-262.
38. Kayaer K, Yildirim T. Medical diagnosis on Pima Indian diabetes using general regression neural networks. Proceedings of the international conference on artificial neural networks and neural information processing, Istanbul, 2003, 181-184.
39. Temurtas H, Yumusak N, Temurtas F. A comparative study on diabetes disease diagnosis using neural networks. Expert Syst Appl 2009. 36:8610-8615.
40. Specht DF. Probabilistic neural networks. Neural Netw 1990. 3:109-118.
41. Buhmann, Martin D. Radial basis functions: theory and implementations. Cambridge University Press, 2003. pp 54-78.
42. Hagan MT, Demuth HB, Beale M. Neural network design. PWS Publishing Company, 1996. pp 102-108.
43. Bart K. Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hall, 1992. pp 36-49.
44. Polat K, Gunes S, Arslan A. A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine. Exp Syst Appl 2008. 34:482-487.
45. Dogantekin E, Dogantekin A, Avci D, et al. An intel ligent diagnosis system for diabetes on Linear Discriminant Analysis and Adaptive Network Based Fuzzy Inference System: LDA-ANFIS. Digit Signal Process 2009. 20:1248-1255.
46. Smith JW, Everhart JE, Dickson WC, et al. Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. Proceedings of the Symposium on Computer Applications and Medical Care, IEEE Computer Society Press, 1988, 261-265.
47. Carpenter GA, Markuzon N. ARTMAP-IC and medical diagnosis: instance counting and inconsistent cases. Neural Netw 1998. 11:323-336.
48. Jacobs RA, Jordan MI, Nowlan SJ, et al. Adaptive mixtures of local experts. Neural Comput 1991. 3:79-87.
49. Ubeyli ED. Modified mixture of experts for diabetes diagnosis. J Med Syst 2009. 33:299-305.
50. Chen K. A connectionist method for pattern classification with diverse features. Pattern Recognit Lett 1998. 19:7545-558.
51. Kamruzzaman SM, Hasan AR, Siddiquee AB, et al. Medical diagnosis using neural network. Proceedings of the 3rd International Conference on Electrical and Computer Engineering, Dhaka, Bangladesh, 2004. 537-540.
52. Ubeyli ED. Automatic diagnosis of diabetes using adaptive neuro-fuzzy inference systems. Expert Syst 2010. 27:259-266.
53. Stoean C, Stoean R, Preuss M, et al. Diabetes diagnosis through the means of a multimodal evolutionary algorithm. Proceedings of the 1st East European Conference on Health Care Modelling and Computation, Craiova, Romania, Craiova, Medical University Press, 2005, 277-289.
54. Shang N, Breiman L. Distribution based trees are more accurate. Proceedings of ICONIP 96, Springer, Hong Kong, 1996, 133-138.
55. Polat K, Gunes S. An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digit Sign Proc 2007. 17:702-710.
56. Friedman N, Geiger D, Goldszmit M. Bayesian networks classifiers. Mach Learn 1997. 29:131-163.