Data mining for the diagnosis of type II diabetes from three-dimensional body surface anthropometrical scanning data

Computers and Mathematics with Applications

Volumn 51, Issue 6-7, 2006, Pages 1075-1092

  Su, Chad Ton ^a   Yang, Chien Hsin ^b   Hsu, Kuang Hung ^c   Chiu, Wen Ko ^d  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^b NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^c CHANG GUNG UNIVERSITY   (Taiwan)

^d CHANG GUNG UNIVERSITY   (Taiwan)

Author keywords

Backpropagation neural network; Data mining; Diagnosis; Type II diabetes

Indexed keywords

BACKPROPAGATION; BODY FLUIDS; DECISION THEORY; DIAGNOSIS; DISEASES; GLUCOSE; NEURAL NETWORKS; SCANNING; THREE DIMENSIONAL;

BACKPROPAGATION NEURAL NETWORK; DECISION TREE; PREVENTIVE MEDICINE; TYPE II DIABETES;

DATA MINING;

EID: 33745826641     PISSN: 08981221     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.camwa.2005.08.034     Document Type: Article

References (34)

1. Canadian Diabetes Association. Diabetes Dictionary (2003). http://www.diabetes.ca
2. Kim Y.I., Kim C.H., Choi C.S., Chung Y.E., Lee M.S., Lee S.I., Park J.Y., Hong S.K., and Lee K.U. Microalbuminuria is associated with the insulin resistance syndrome independent of hypertension and type 2 diabetes in the Korean population. Diabetes Research and Clinical Practice 52 (2001) 145-152
3. Chen C.H., Lin K.C., Tsai S.T., and Chou P. Different association of hypertension and insulin-related metabolic syndrome between man and women in 8437 nondiabetic Chinese. American Journal of Hypertension 13 7 (2000) 846-853
4. Dorffner G., and Porenta G. On using feedforward neural networks for clinical diagnostic tasks. Artificial Intelligence in Medicine 6 (1994) 417-435
5. Fogel D.B., Wasson III E.C., Boughton E.M., and Porto V.W. Evolving artificial neural networks for screening features from mammograms. Artificial Intelligence in Medicine 14 (1998) 317-326
6. Folland R., Hines E., Dutta R., Boilot R., and Morgan D. Comparison of neural network predictors in the classification of tracheal-bronchial breath sounds by respiratory auscultation. Artificial Intelligence in Medicine 31 (2004) 211-220
7. Markey M.K., Lo J.Y., Tourassi G.D., and Floyd Jr. C.E. Self-organizing map for cluster analysis of a breast cancer database. Artificial Intelligence in Medicine 27 (2003) 113-127
8. Carlos A., Pena R., and Moshe S. Evolutionary computation in medicine: An overview. Artificial Intelligence in Medicine 19 (2000) 1-23
9. Kukar M., Kononenko I., Grošelj C., Kralj K., and Fettich J. Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artificial Intelligence in Medicine 16 (1999) 25-50
10. Mizoguchi F., Ohwada H., Daidoji M., and Shirato S. Using inductive logic programming to learn classification rules that identify glaucomatous eyes. In: Lavrač N., Keravnou E., and Zupan B. (Eds). Intelligent Data Analysis in Medicine and Pharmacology (1997), Kluwer 227-242
11. Shankle W.R., Mani S., Pazzani M.J., and Smyth P. Dementia screening with machine learning methods. In: Lavrač N., Keravnou E., and Zupan B. (Eds). Intelligent Data Analysis in Medicine and Pharmacology (1997), Kluwer 149-166
12. Erwin T., Reinhard M., Karl S., and Gilbert R. The determination of three subcutaneous adipose tissue compartments in non-insulin-dependent diabetes mellitus women with artificial neural networks and factor analysis. Artificial Intelligence in Medicine 17 (1999) 181-183
13. Erwin T., Reinhard M., Karl S., and Gilbert R. Artificial neural networks compared to factor analysis for low-dimensional classification of high-dimensional body fat topography data of healthy and diabetic subjects. Computers and Biomedical Research 33 (2000) 365-374
14. Lucas P.J.F. Analysis of notions of diagnosis. Artificial Intelligence 105 12 (1998) 295-343
15. Abdolmaleki P., Buadu L.D., and Naderimansh H. Feature extraction and classification of breast cancer on dynamic magnetic resonance imaging using artificial neural network. Cancer Letters 171 2 (2001) 183-191
16. Fausett L. Fundamentals of Neural Networks (1994), Prentice-Hall International
17. Tsukimoto H. Extraction rules from trained neural networks. IEEE Transactions on Neural Networks 11 (2000) 377-389
18. Andrews R., Diederich J., and Tickle A.B. Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based System 8 (1995) 373-389
19. Andrews R., and Diederich J. Rules and networks. Proceedings of Rule Extraction Trained Artificial Neural Networks Workshop (1996), AISB
20. Su C.T., Hsu H.H., and Tsai C.H. Knowledge mining from trained neural networks. Journal of Computer Information Systems 42 4 (2002) 61-70
21. Han J.W., and Kamber M. Data Mining: Concepts and Techniques (2001), Morgan Kaufmann, San Francisco, CA
22. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufmann, San Mateo, CA
23. Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. Classification and Regression Trees (1984), Wadsworth Int., Belmont, CA
24. Quinlan J.R. Induction of decision trees. Machine Learning 1 1 (1986) 81-106
25. Kleinbaum D.G. Logistic regression: A self-learning text (2002), Springer-Verlag, New York
26. Pawlak Z. Why rough sets. Proceedings of the Fifth IEEE International Conference on Fuzzy Systems 2 (1996) 738-743
27. Carnegie, PA. NeuralWare. NeuralWorks Professional II/Plus getting started: A tutorial for Microsoft Windows computers, Version 5.50 (2001)
28. Rosetta GUI Version 1.4.41 http://idi.ntnu.no/≈aleks/rosetta/, NTNU, Norway, (2001).
29. NcNeely M.J., Boyko E.J., Shofer J.B., Newell-Morris L., Leonetti D.L., and Fujimoto W.Y. Standard definitions of overweight and central adiposity for determining diabetes risk in Japanese Americans. American Journal of Clinical Nutrition 74 (2001) 101-104
30. Nagaretani H., Nakamura T., Funabashi T., Kotani K., Miyanaga M., Togunaga K., Takahasi M., Nishizawa H., Kishida K., Kuriyama H., Hotta K., Yamashita S., and Matsuzawa Y. Visceral fat is major contribution for multiple risk clustering in Japanese men with impaired glucose to tolerance. Diabetes Care 24 (2001) 2127-2133
31. (Supplement). Norgan N.G. Population difference in body composition in relation to the body mass index. European Journal of Clinical Nutrition 48 (1994) S10-S25
32. Wang J., Thornton J.C., Russel M., Burastero S., Heymsfield S.B., and Pierson R.N. Asians have lower BMI but higher percentage body fat than do white: Comparison of anthropometric measurements. American Journal Clinical Nutrition 60 (1994) 23-28
33. Gallagher D., Visser M., Sepulveda D., Pierson R.N., Harris T., and Heymsfield S.B. How useful is BMI for comparison of body fatness across age, sex and ethic groups. American Journal of Epidemiology 143 (1996) 228-239
34. Kissebah A.H., Freedman D.S., and Peiris A.N. Health risks of obesity. Medicine Clinics of North America 73 (1989) 111-138