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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 242717, 7 pages
http://dx.doi.org/10.1155/2014/242717
Research Article

Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults

1School of Nutrition and Dietetics, College of Health Professions, The University of Akron, Akron, OH 44325-6102, USA
2Department of Statistics, College of Arts and Sciences, University of Akron, Akron, OH 44325-1913, USA

Received 21 January 2014; Accepted 16 March 2014; Published 10 April 2014

Academic Editor: Zhenyu Jia

Copyright © 2014 Brian Miller et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. K. G. M. M. Alberti, R. H. Eckel, S. M. Grundy et al., “Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity,” Circulation, vol. 120, no. 16, pp. 1640–1645, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. K. G. M. M. Alberti and P. Zimmet, “The metabolic syndrome—a new worldwide definition,” The Lancet, vol. 366, no. 9491, pp. 1059–1062, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. T. Bloomgarden, “Consequences of diabetes: cardiovascular disease,” Diabetes Care, vol. 27, no. 7, pp. 1825–1831, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Ärnlöv, E. Ingelsson, J. Sundström, and L. Lind, “Impact of body mass index and the metabolic syndrome on the risk of cardiovascular disease and death in middle-aged men,” Circulation, vol. 121, no. 2, pp. 230–236, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. P. Y. Liu, L. M. Hornbuckle, L. B. Panton, J. S. Kim, and J. Z. Ilich, “Evidence for the association between abdominal fat and cardiovascular risk factors in overweight and obese African American women,” Journal of the American College of Nutrition, vol. 31, no. 2, pp. 126–132, 2012. View at Publisher · View at Google Scholar
  6. A. A. Motala, T. Esterhuizen, F. J. Pirie, and M. A. K. Omar, “The prevalence of metabolic syndrome and determination of the optimal waist circumference cutoff points in a rural South African community,” Diabetes Care, vol. 34, no. 4, pp. 1032–1037, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. D. M. Boudreau, D. C. Malone, M. A. Raebel et al., “Health care utilization and costs by metabolic syndrome risk factors,” Metabolic Syndrome and Related Disorders, vol. 7, no. 4, pp. 305–313, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. R. L. Coleman, R. J. Stevens, R. Retnakaran, and R. R. Holman, “Framingham, SCORE, and DECODE risk equations do not provide reliable cardiovascular risk estimates in type 2 diabetes,” Diabetes Care, vol. 30, no. 5, pp. 1292–1294, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. A. H. Gandomi, M. M. Fridline, and D. A. Roke, “Decision tree approach for soil liquefaction assessment,” The Scientific World Journal, vol. 2013, Article ID 346285, 8 pages, 2013. View at Publisher · View at Google Scholar
  10. A. Worachartcheewan, C. Nantasenamat, C. Isarankura-Na-Ayudhya, P. Pidetcha, and V. Prachayasittikul, “Identification of metabolic syndrome using decision tree analysis,” Diabetes Research and Clinical Practice, vol. 90, no. 1, pp. e15–e18, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. R. L. Sacco, M. Khatri, T. Rundek et al., “Improving global vascular risk prediction with behavioral and anthropometric factors. The multiethnic NOMAS (Northern Manhattan Cohort Study),” Journal of the American College of Cardiology, vol. 54, no. 24, pp. 2303–2311, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. R. B. D'Agostino Sr., R. S. Vasan, M. J. Pencina et al., “General cardiovascular risk profile for use in primary care: the Framingham heart study,” Circulation, vol. 117, no. 6, pp. 743–753, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. P. A. Braveman, C. Cubbin, S. Egerter, D. R. Williams, and E. Pamuk, “Socioeconomic disparities in health in the United States: what the patterns tell us,” American Journal of Public Health, vol. 100, supplement 1, pp. S186–S196, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. A. J. Cameron, P. Z. Zimmet, J. E. Shaw, and K. G. M. M. Alberti, “The metabolic syndrome: in need of a global mission statement,” Diabetic Medicine, vol. 26, no. 3, pp. 306–309, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. Centers for Disease Control and Prevention (CDC) and National Center for Health Statistics, 2007-2008 National Health and Nutrition Examination Survey: Survey Operations Manuals, Brochures, Consent Documents, U.S. Department of Health and Human Services, 2013, http://www.cdc.gov/nchs/nhanes/nhanes2007-2008/current_nhanes_07_08.htm.
  16. World Health Organization, Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation, Geneva, 8–11 December 2008, World Health Organization, Geneva, Switzerland, 2008.
  17. G. V. Kass, “An exploratory technique for investigating large quantities for categorical data,” Applied Statistics, vol. 20, pp. 119–127, 1980. View at Google Scholar
  18. M. Dehghan and A. T. Merchant, “Is bioelectrical impedance accurate for use in large epidemiological studies?” Nutrition Journal, vol. 7, no. 1, article 26, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. V. L. Roger, A. S. Go, D. M. Lloyd-Jones et al., “Heart disease and stroke statistics—2011 update: a report from the american heart association,” Circulation, vol. 123, no. 4, pp. e18–e209, 2011. View at Publisher · View at Google Scholar
  20. A. Worachartcheewan, P. Dansethakul, C. Nantasenamat, P. Pidetcha, and V. Prachayasittikul, “Determining the optimal cutoff points for waist circumference and body mass index for identification of metabolic abnormalities and metabolic syndrome in urban Thai population,” Diabetes Research and Clinical Practice, vol. 98, no. 2, pp. e16–e21, 2012. View at Publisher · View at Google Scholar
  21. T. Kawada, T. Otsuka, H. Inagaki et al., “Optimal cut-off levels of body mass index and waist circumference in relation to each component of metabolic syndrome (MetS) and the number of MetS component,” Diabetes and Metabolic Syndrome: Clinical Research and Reviews, vol. 5, no. 1, pp. 25–28, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. J.-P. Després, L. van Gaal, X. Pi-Sunyer, and A. Scheen, “Efficacy and safety of the weight-loss drug rimonabant,” The Lancet, vol. 371, no. 9612, p. 555, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. K. Lee, S. Lee, Y.-J. Kim, and Y.-J. Kim, “Waist circumference, dual-energy X-ray absortiometrically measured abdominal adiposity, and computed tomographically derived intra-abdominal fat area on detecting metabolic risk factors in obese women,” Nutrition, vol. 24, no. 7-8, pp. 625–631, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. P. Hari, K. Nerusu, V. Veeranna et al., “A gender-stratified comparative analysis of various definitions of metabolic syndrome and cardiovascular risk in a multiethnic U.S. population,” Metabolic Syndrome and Related Disorders, vol. 10, no. 1, pp. 47–55, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. S. E. Walker, M. J. Gurka, M. N. Oliver, D. W. Johns, and M. D. DeBoer, “Racial/ethnic discrepancies in the metabolic syndrome begin in childhood and persist after adjustment for environmental factors,” Nutrition, Metabolism and Cardiovascular Diseases, vol. 22, no. 2, pp. 141–148, 2012. View at Publisher · View at Google Scholar · View at Scopus