Artificial Intelligence in Medicine
Volume 38, Issue 3 , Pages 275-289 , November 2006

Using classification trees to assess low birth weight outcomes

  • Panagiota Kitsantas

      Affiliations

    • George Mason University, Department of Health Administration and Policy, The College of Health and Human Services, 4400 University Drive, Fairfax, VA 22030, USA
    • Corresponding Author InformationCorresponding author. Tel.: +1 703 993 1901; fax: +1 703 993 1953.
    • ,
  • Myles Hollander

      Affiliations

    • Florida State University, Department of Statistics, Tallahassee, FL 32306, USA
    • ,
  • Lei Li

      Affiliations

    • University of Southern California, Department of Biology and Mathematics, Los Angeles, CA 90089, USA

Received 7 December 2005 ,Revised 24 March 2006 ,Accepted 29 March 2006.

References 

  1. Hack M, Taylor G, Klein N. School-age outcomes in children with birth weights under 750g. N Engl J Med. 1994;331:753–759
  2. Lewit EM, Baker LS, Corman H, Shiono PH. The direct costs of low birth weight. Future Child. 1995;5:35–51
  3. Jason CJ, Samuhel ME, Glick BJ, Welsh AK. Geographic distribution of unexplained low birth weight. J Occup Med. 1986;28:728–740
  4. Cramer JC. Social factors and infant mortality: identifying high-risk groups and proximate causes. Demography. 1987;24:299–322
  5. Shiono PH, Behrman RE. Low birth weight: analysis and recommendations. Future Child. 1995;5:150–167
  6. Guyer B, Hoyert DL, Martin JA, Ventura SJ, MacDorman MF, Strobino DM. Annual summary of vital statistics. Pediatrics. 1999;104:1229–1246
  7. Kessel SS, Kleinman JC, Koontz AM, Hogue CJR, Berendes HW. Racial differences in pregnancy outcomes. Clin Perinatol. 1988;15:745–754
  8. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. California: Wadsworth; 1984;
  9. Frisbie WP, Forbes D, Pullum SG. Compromised birth outcomes and infant mortality among racial and ethnic groups. Demography. 1996;33:469–481
  10. CART. Tree-Structured Non-Parametric Data Analysis Version 4.0. San Diego: Salford Systems; 2000.
  11. Hosmer DW, Lemeshow S. Applied logistic regression. New York: Wiley; 2000;
  12. STATA. Statistical Software Version 7.0. College Station: STATA Corporation; 2001.
  13. David RJ, Collins JW. Differing birth weight among infants of U.S.-born blacks, African-born blacks, and U.S.-born whites. N Engl J Med. 1997;337:1232–1233
  14. Kallan JE. Race, intervening variables, and two components of low birth weight. Demography. 1993;3:489–506
  15. Liberatos P, Link B, Kelsey J. Measurement of social class in epidemiology. Epidemiol Rev. 1988;10:87–121
  16. Winkleby M, Jatulis D, Frank E, Fortmann S. Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease. Am J Public Health. 1992;82:816–820
  17. Goldhagen J, Remo R, Bryant T, Wludyka P, Dailey A, Woode D, et al. The health status of southern children: a neglected regional disparity. Pediatrics. 2005;116:746–753
  18. Kitsantas P, Hollander M, Li L. Stability of classification trees. In: Ermakov SM, Melas VB, Pepelyshev AN, editors. Proceedings of the Fifth St. Petersburg Workshop on Simulation. NII Chemistry Saint Petersburg University Publishers; 2005. p. 359–66.
  19. Kuhnert PM, Do KA, McClure R. Combining non-parametric models with logistic regression: an application to motor vehicle injury data. Comput Statist Data Anal. 2000;34:371–386
  20. Werneck GL, De Carvalho DM, Barroso DE, Cook EF, Walker AM. Classification trees and logistic regression applied to prognostic studies: a comparison using meningococcal disease as an example. J Trop Pediatr. 1999;45:248–251
  21. Marshall RJ. The use of classification and regression trees in clinical epidemiology. J Clin Epidemiol. 2001;54:603–609
  22. Harper PR. A review and comparison of classification algorithms for medical decision making. Health Policy. 2005;71:315–331
  23. U.S. Department of Health and Human Services, Office of Disease Prevention and Health Promotion: Healthy People 2010: Understanding and Improving Health and Objectives for Improving Health. Washington, DC: US Department of Health and Human Services; 2000.
  24. Raymond EG, Tafari N, Troendle JF, Clemens JD. Development of a practical screening tool to identify preterm, low-birthweight neonates in Ethiopia. Lancet. 1994;344:524–527
  25. Zhang H, Bracken MB. Tree-based risk factor analysis of preterm delivery and small-for-gestational-age birth. Am J Epidemiol. 1995;141:70–78
  26. Zhang H, Bracken MB. Tree-based, two-stage factor analysis for spontaneous abortion. Am J Epidemiol. 1996;144:989–996
  27. Toschke AM, Beyerlein A, von Kries R. Childern at high risk for overweight: a classification and regression trees analysis approach. Obes Res. 2005;13:1270–1274
  28. Hastie T, Tibshirani R, Friedman JH . The elements of statistical learning. Berlin: Springer-Verlag; 2001;
  29. Zhang H, Singer B. Recursive partitioning in the health sciences. New York: Springer-Verlag; 1999;
  30. Raubertas RF, Rodewald LE, Humiston SG, Szilagyi PG. ROC curves for classification trees. Med Decision Making. 1994;14:169–174
  31. Colombet I, Ruelland A, Chatellier G, Gueyffier F, Degoulet P. Models to predict cardiovascular risk: comparison of CART, multilayer perception and logistic regression. J Am Med Inform Assoc. 2000;S208:156–160
  32. Bazarian JJ, Atabaki S. Predicting syndrome after minor traumatic brain injury. Acad Emerg Med. 2001;8:788–795

PII: S0933-3657(06)00058-3

doi: 10.1016/j.artmed.2006.03.008

Artificial Intelligence in Medicine
Volume 38, Issue 3 , Pages 275-289 , November 2006

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