A hybrid intelligent system for diagnosing microalbuminuria in type 2 diabetes patients without having to measure urinary albumin

Computers in Biology and Medicine

Volumn 45, Issue 1, 2014, Pages 34-42

  Marateb, Hamid R ^a   Mansourian, Marjan ^b   Faghihimani, Elham ^c   Amini, Masoud ^c   Farina, Dario ^d  

^a UNIVERSITY OF ISFAHAN   (Iran)

^b ISFAHAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^c ISFAHAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^d UNIVERSITY MEDICAL CENTER GÖTTINGEN   (Germany)

Author keywords

Classification; Diabetes type 2; Expert based system; Fuzzy rule induction; Microalbuminuria; Particle swarm optimization; Statistical feature extraction

Indexed keywords

CARDIOVASCULAR MORTALITY; EXPERT-BASED SYSTEM; FUZZY CLASSIFIER SYSTEMS; FUZZY RULE INDUCTION; HYBRID INTELLIGENT SYSTEM; MICROALBUMINURIA; MULTIPLE LOGISTIC REGRESSION; STATISTICAL FEATURE EXTRACTIONS;

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; FUZZY SETS; GOLD; INTELLIGENT SYSTEMS; PARTICLE SWARM OPTIMIZATION (PSO); TOOLS;

DIAGNOSIS;

ALBUMIN; GLUCOSE; HEMOGLOBIN A1C;

ACCURACY; ADULT; ARTICLE; BODY MASS; CONTROLLED STUDY; CROSS-SECTIONAL STUDY; DIABETIC PATIENT; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; FUZZY SYSTEM; GLUCOSE BLOOD LEVEL; HUMAN; HYBRID INTELLIGENT SYSTEM; MAJOR CLINICAL STUDY; MALE; MICROALBUMINURIA; NON INSULIN DEPENDENT DIABETES MELLITUS; PRIORITY JOURNAL; PROTEIN URINE LEVEL; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY;

CLASSIFICATION; DIABETES TYPE 2; EXPERT-BASED SYSTEM; FUZZY RULE INDUCTION; MICROALBUMINURIA; PARTICLE SWARM OPTIMIZATION; STATISTICAL FEATURE EXTRACTION;

ADULT; AGED; ALBUMINURIA; DIABETES MELLITUS, TYPE 2; FEMALE; FUZZY LOGIC; HUMANS; MALE; MIDDLE AGED; MODELS, BIOLOGICAL; MODELS, STATISTICAL; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84890213426     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2013.11.006     Document Type: Article

References (72)

1. Gardner D., Shoback D. Greenspan's Basic and Clinical Endocrinology 2011, McGraw-Hill Medical. 9th ed.
2. Mathers C.D., Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006, 3:e442.
3. Schlienger J.L. Type 2 diabetes complications. Presse Med. 2013, 42:839-848.
4. Hellemons M.E., Kerschbaum J., Bakker S.J., Neuwirt H., Mayer B., Mayer G., de Zeeuw D., Heerspink H.J.Lambers, Rudnicki M. Validity of biomarkers predicting onset or progression of nephropathy in patients with Type 2 diabetes: a systematic review. Diabet. Med.: J. Br. Diabet. Assoc. 2012, 29:567-577.
5. Winocour P.H., Marshall S.M. Microalbuminuria: Biochemistry, Epidemiology and Clinical Practice 1998, Cambridge University Press, United Kingdom.
6. Ritz E. Nephropathy in type 2 diabetes. J. Intern. Med. 1999, 245:111-126.
7. Raine A.E. Epidemiology, development and treatment of end-stage renal failure in type 2 (non-insulin-dependent) diabetic patients in Europe. Diabetologia 1993, 36:1099-1104.
8. Parving H.H., Lewis J.B., Ravid M., Remuzzi G., Hunsicker L.G. Prevalence and risk factors for microalbuminuria in a referred cohort of type II diabetic patients: a global perspective. Kidney Int. 2006, 69:2057-2063.
9. Cederholm J., Eliasson B., Nilsson P.M., Weiss L., Gudbjornsdottir S. Microalbuminuria and risk factors in type 1 and type 2 diabetic patients. Diabet. Res. Clin. Pract. 2005, 67:258-266.
10. Mongkolsomlit S., Patumanond J., Tawichasril C., Komoltri C., Rawdaree P. Meta-regression of risk factors for microalbuminuria in type 2 diabetes. Southeast Asian J. Trop. Med. Public Health 2012, 43:455-466.
11. Higgins J.P. Nonlinear systems in medicine. Yale J. Biol. Med. 2002, 75:247-260.
12. Sarafidis P.A., Riehle J., Bogojevic Z., Basta E., Chugh A., Bakris G.L. A comparative evaluation of various methods for microalbuminuria screening. Am. J. Nephrol. 2008, 28:324-329.
13. D. Atlas, International Diabetes Federation, Hallado en:, 2000. http://www.idf.org/diabetesatlas/5e/es/prologo.
14. Amini M., Parvaresh E. Prevalence of macro- and microvascular complications among patients with type 2 diabetes in Iran: a systematic review. Diabet. Res. Clin. Pract. 2009, 83:18-25.
15. Lavrac N. Selected techniques for data mining in medicine. Artif. Intell. Med. 1999, 16:3-23.
16. Kotsiantis S.B. Supervised Machine Learning: A Review of Classification Techniques 2007, 3-24. IOS Press, Netherlands,.
17. Babuška R., Verbruggen H. Neuro-fuzzy methods for nonlinear system identification. Annu. Rev. Control 2003, 27:73-85.
18. Data Mining Techniques for the Life Sciences, Humana Press, 2010.
19. A. Lorenz, M. Blum, H. Ermert, T. Senge, Comparison of different Neuro-Fuzzy Classification Systems for the Detection of Prostate Cancer in Ultrasonic Images, vol. 1202, 1997, pp. 1201-1204.
20. V. Neagoe, I. Iatan, S. Grunwald, A neuro-fuzzy approach to classification of ECG signals for ischemic heart disease diagnosis, in: AMIA. Annual Symposium Proceedings/AMIA Symposium, 2003, pp. 494-498.
21. Wang L.-X. A Course in Fuzzy Systems and Control 1997, Prentice-Hall, Inc., N.J.
22. Abraham A., Grosan C., Ramos V. Swarm Intelligence in Data Mining 2006, Springer, Berlin, New York.
23. Juang C.-F., Chang P.-H. Designing fuzzy-rule-based systems using continuous ant-colony optimization,. IEEE Trans. Fuzzy Syst. 2010, 18:138-149.
24. Zheng D., Kainz W. Fuzzy Rule Extraction from GIS Data with a Neural Fuzzy System for Decision Making 1999, 79-84. ACM, Kansas City, Missouri, USA.
25. J. Kennedy, Particle swarm optimization, in: C. Sammut, G. Webb (ed.), Encyclopedia of Machine Learning, Springer, 2010, pp. 760-766.
26. Kennedy J.F., Eberhart R.C., Shi Y. Swarm Intelligence 2001, Morgan Kaufmann Publishers, San Francisco.
27. Marateb H.R., McGill K.C. Resolving superimposed MUAPs using particle swarm optimization. IEEE Trans. Bio-med. Eng. 2009, 56:916-919.
28. Poli R. Analysis of the publications on the applications of particle swarm optimisation. J. Artif. Evol. Appl. 2008, 2008:3.
29. Omran M.G., Engelbrecht A.P., Salman A. A color image quantization algorithm based on particle swarm optimization. Informatica (Ljubljana) 2005, 29:261.
30. van den Bergh F., Engelbrecht A.P. A study of particle swarm optimization particle trajectories. Inf. Sci. 2006, 176:937-971.
31. Bergh F.V.D. An Analysis of Particle Swarm Optimizers 2002, 1. University of Pretoria, South Africa,.
32. Banks A., Vincent J., Anyakoha C. A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat. Comput. 2007, 7:109-124.
33. R.C. Eberhart, Y. Shi, Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization, IEEE, 2000, pp. 84-88.
34. Clerc M. From theory to practice in particle swarm optimization. Handbook of Swarm Intelligence 2010, 3-36. Springer, Berlin, Heidelberg. B. Panigrahi, Y. Shi, M.-H. Lim (Eds.).
35. Arevalillo J.M., Navarro H. A new method for identifying bivariate differential expression in high dimensional microarray data using quadratic discriminant analysis. BMC Bioinf. 2011, 12(Suppl 12):S6.
36. Grouven U., Bergel F., Schultz A. Implementation of linear and quadratic discriminant analysis incorporating costs of misclassification. Comput. Methods Programs Biomed. 1996, 49:55-60.
37. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2000, Wiley, New York. 2nd ed.
38. Vapnik V.N. Statistical Learning Theory 1998, Wiley, Danvers, MA.
39. Wu K.-P., Wang S.-D. Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recognition 2009, 42:710-717.
40. Platt J.C. Fast training of support vector machines using sequential minimaloptimization. Advances in Kernel Methods---Support Vector Learning 1999, 185-208. MIT Press.
41. Botev Z.I., Grotowski J.F., Kroese D.P. Kernel density estimation via diffusion. Ann. Stat. 2010, 38:2916-2957.
42. Stiglic G., Kocbek S., Pernek I., Kokol P. Comprehensive decision tree models in bioinformatics. PloS ONE 2012, 7:e33812.
43. Breiman L. Bagging predictors. Mach. Learn. 1996, 24:123-140.
44. S. Salzberg, C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993, Machine Learning, vol. 16, 1994, pp. 235-240.
45. Jang J.S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 1993, 23:665-685.
46. Terano T., Asai K., Sugeno M. Fuzzy Systems Theory and its Applications 1992, Academic Press, Boston.
47. Campello R.J.G.B., Hruschka E.R. A fuzzy extension of the silhouette width criterion for cluster analysis. Fuzzy Sets Syst. 2006, 157:2858-2875.
48. Dash M., Liu H. Feature selection for classification. Intell. Data Anal. 1997, 1:131-156.
49. Hellevik O. Linear versus logistic regression when the dependent variable is a dichotomy. Qual. Quantity 2009, 43:59-74.
50. Zhao L., Chen Y., Schaffner D.W. Comparison of logistic regression and linear regression in modeling percentage data. Appl. Environ. Microbiol. 2001, 67:2129-2135.
51. Glidden D.V., Shiboski S.C., McCulloch C.E. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models 2011, Springer, United Kingdom.
52. Tipping M.E. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 2001, 1:211-244.
53. F. Li, Y. Yang, E.P. Xing, From Lasso Regression to Feature Vector Machine, 2005, pp. 779-786.
54. Nilsson R. Statistical Feature Selection with Applications in Life Science 2007, 191. Linkoping University, Sweden.
55. A.R. Webb, K.D. Copsey, Performance assessment, in: Statistical Pattern Recognition, John Wiley & Sons, Ltd., 2011, pp. 404-432.
56. Sammut C., Webb G.I. Encyclopedia of Machine Learning 2010, Springer, New York, London.
57. Swets J.A. Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers 1996, Lawrence Erlbaum Associates, Inc, Mahwah, N.J.
58. J.P. Egan, Signal Detection Theory and {ROC} Analysis, 1975.
59. Dietterich T.G. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 1998, 10:1895-1923.
60. Carletta J. Assessing agreement on classification tasks: the Kappa statistic. Comput. Linguist. 1996, 22:249-254.
61. American Diabetes Association American Diabetes Association Complete Guide to Diabetes 2005, American Diabetes Association, Alexandria, VA. 4th ed.
62. Mattix H.J., Hsu C.Y., Shaykevich S., Curhan G. Use of the albumin/creatinine ratio to detect microalbuminuria: implications of sex and race. J. Am. Soc. Nephrol.: JASN 2002, 13:1034-1039.
63. Keane W.F., Eknoyan G. Proteinuria, albuminuria, risk, assessment, detection, elimination (PARADE): a position paper of the National Kidney Foundation. Am. J. Kidney Dis.: Off. J. Natl. Kidney Found. 1999, 33:1004-1010.
64. F. Mosteller, A k-sample slippage test for an extreme population, in: Selected Papers of Frederick Mosteller, Springer, 2006, pp. 101-109.
65. Webb A.R. Statistical Pattern Recognition 2003, Wiley. com. Malvern, UK.
66. Schmitz A., Vaeth M., Mogensen C. Systolic blood pressure relates to the rate of progression of albuminuria in NIDDM. Diabetologia 1994, 37:1251-1258.
67. W.H. Organization, Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia: Report of a WHO/IDF Consultation, Geneva, World Health Organization, 2006, pp. 1-50.
68. Vijan S. Type 2 diabetes. Ann. Intern. Med. 2010, 152. (ITC31-15; quiz ITC316).
69. Alberti K.G.M.M., Zimmet P. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Diabet. Med. 1998, 15:539-553.
70. Valdez R. Detecting undiagnosed type 2 diabetes: family history as a risk factor and screening tool. J. Diabet. Sci. Technol. 2009, 3:722-726.
71. Teodorescu H.-N.L., Kandel A., Jain L.C. Fuzzy and Neuro-Fuzzy Systems in Medicine 1999, CRC Press, Florida, US.
72. Nauck D., Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artif. Intell. Med. 1999, 16:149-169.