Journal of Clinical Epidemiology
Volume 57, Issue 6 , Pages 551-560 , June 2004

Genetic programming outperformed multivariable logistic regression in diagnosing pulmonary embolism

  • Cornelis J Biesheuvel

      Affiliations

    • Julius Center for Health Sciences and Primary Care, University Medical Center, P.O. Box 85500, GA Utrecht 3508, The Netherlands
    • Corresponding Author InformationCorresponding author. Tel.: +31-30-2538633; fax: +31-30-2505480.
  • ,
  • Ivar Siccama

      Affiliations

    • KiQ Ltd., De Lairessestraat 150, 1075 HL Amsterdam, The Netherlands
  • ,
  • Diederick E Grobbee

      Affiliations

    • Julius Center for Health Sciences and Primary Care, University Medical Center, P.O. Box 85500, GA Utrecht 3508, The Netherlands
  • ,
  • Karel G.M Moons

      Affiliations

    • Julius Center for Health Sciences and Primary Care, University Medical Center, P.O. Box 85500, GA Utrecht 3508, The Netherlands

,Accepted 23 October 2003.

References 

  1. Spiegelhalter DJ. Probabilistic prediction in patient management and clinical trials. Stat Med. 1986;5:421–433
  2. Hosmer D, Lemeshow S. Applied logistic regression. New York: John Wiley & Sons, Inc; 1989;
  3. Simon R, Altman DG. Statistical aspects of prognostic factor studies in oncology. Br J Cancer. 1994;69:979–985
  4. Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15:361–387
  5. Laupacis A, Sekar N, Stiell IG. Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA. 1997;277:488–494
  6. Harrell FE. Regression modeling strategies. New York: Springer-Verlag; 2001;
  7. Moons KG, Grobbee DE. Diagnostic studies as multivariable, prediction research. J Epidemiol Community Health. 2002;56:337–338
  8. Selker HP, Griffith JL, Patil S, Long WJ, D'Agostino RB. A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients. J Investig Med. 1995;43:468–476
  9. Tsien CL, Fraser HS, Long WJ, Kennedy RL. Using classification tree and logistic regression methods to diagnose myocardial infarction. Medinfo. 1998;9(Pt 1):493–497
  10. Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol. 1996;49:1225–1231
  11. Ennis M, Hinton G, Naylor D, Revow M, Tibshirani R. A comparison of statistical learning methods on the Gusto database. Stat Med. 1998;17:2501–2508
  12. Ottenbacher KJ, Smith PM, Illig SB, Linn RT, Fiedler RC, Granger CV. Comparison of logistic regression and neural networks to predict rehospitalization in patients with stroke. J Clin Epidemiol. 2001;54:1159–1165
  13. Resnic FS, Ohno-Machado L, Selwyn A, Simon DI, Popma JJ. Simplified risk score models accurately predict the risk of major in-hospital complications following percutaneous coronary intervention. Am J Cardiol. 2001;88:5–9
  14. Holland JH. Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press; 1975;
  15. Goldberg DE. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison Wesley Publishing Company; 1989;
  16. Koza JR. Genetic programming III. Cambridge, MA: MIT Press; 1999;
  17. Knottnerus JA. Application of logistic regression to the analysis of diagnostic data: exact modeling of a probability tree of multiple binary variables. Med Decis Making. 1992;12:93–108
  18. van Beek EJR, Kuyer PMM, Schenk BE, Brandjes DPM, ten Cate JW, Büller HR. A normal perfusion lung scan in patients with clinically suspected pulmonary embolism: frequency and clinical validity. Chest. 1995;108:170–173
  19. van Beek EJR, Kuijer PMM, Büller HR, Brandjes DPM, Bossuyt PMM, ten Cate JW. The clinical course of patients with suspected pulmonary embolism. Arch Intern Med. 1997;157:2593–2598
  20. Turkstra F, Kuijer PMM, van Beek EJR, Brandjes DPM, ten Cate JW, Buller HR. Diagnostic utility of ultrasonography of leg veins in patients suspected of having pulmonary embolism. Ann Intern Med. 1997;126:775–781
  21. Miniati M, Prediletto R, Formichi B, Marini C, Di Ricco G, Tonelli L, et al. Accuracy of clinical assessment in the diagnosis of pulmonary embolism. Am J Respir Crit Care Med. 1999;159:864–871
  22. Stollberger C, Finsterer J, Lutz W, Stoberl C, Kroiss A, Valentin A, et al. Multivariate analyses-based prediction rule for pulmonary embolism. Thromb Res. 2000;97:267–273
  23. Wells PS, Ginsberg JS, Anderson DR, Kearon C, Gent M, Turpie AG, et al. Use of a clinical model for safe management of patients with suspected pulmonary embolism. Ann Intern Med. 1998;129:997–1005
  24. Efron B, Tibshirani R. An introduction to the bootstrap. Monographs on statistics and applied probability. New York: Chapman & Hall; 1993;
  25. Steyerberg EW, Harrell FE, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54:774–781
  26. Houwelingen van JC, Le Cessie S. Predictive value of statistical models. Stat Med. 1990;9:1303–1325
  27. Banzhaf W, Nordin P, Keller RE, Francone FD. Genetic programming, an introduction. San Fransisco, CA: Morgan Kaufmann Publishers Inc; 1998;
  28. KiQ Ltd. http://www.kiq.com.

PII: S0895-4356(03)00427-X

doi: 10.1016/j.jclinepi.2003.10.011

Journal of Clinical Epidemiology
Volume 57, Issue 6 , Pages 551-560 , June 2004