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Decision Trees: An Overview and Their Use in Medicine

Journal of Medical Systems volume 26, pages 445–463 (2002)Cite this article

Abstract

In medical decision making (classification, diagnosing, etc.) there are many situations where decision must be made effectively and reliably. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in medicine.

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REFERENCES

  1. Quinlan, J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, 1993.

    Google Scholar 

  2. Quinlan, J. R., Induction of decision trees. Mach. Learn. 1:81-106, 1986.

    Google Scholar 

  3. Quinlan, J. R., Simplifying decision trees, Int. J. Man-Mach. Stud. 27:221-234, 1987.

    Google Scholar 

  4. Shannon, C., and Weaver, W., The Mathematical Theory of Communication, University of Illinois Press, USA, 1949.

    Google Scholar 

  5. Breiman, L., Friedman, J. H., Olsen, R. A., and Stone, C. J., Classification and Regression Trees, Wadsworth, USA, 1984.

  6. Paterson, A., and Niblett, T. B., ACLS Manual, Intelligent Terminals Ltd., Edinburgh, 1982.

    Google Scholar 

  7. Zorman, M., Podgorelec, V., Kokol, P., Peterson, M., and Lane, J., Decision tree's induction strategies evaluated on a hard real world problem. Proc. 13th IEEE Symp. Comp.-Based Med. Syst. (CBMS-2000) pp. 19-24, 2000.

  8. Zorman, M., Hleb S., and Sprogar, M., Advanced tool for building decision trees MtDecit 2.0. Proc. Int. Conf. Artif. Intellig. (ICAI-99), 1999.

  9. Tou, J. T., and Gonzalez, R. C., Pattern Recognition Principles, Addison-Wesley, Reading, MA, 1974.

    Google Scholar 

  10. Murthy, K. V. S., On Growing Better Decision Trees from Data, PhD dissertation, Johns Hopkins University, Baltimore, MD, 1997.

    Google Scholar 

  11. Neapolitan, R., and Naimipour, K., Foundations of Algorithms, D.C. Heath and Company, Lexington, MA, 1996.

    Google Scholar 

  12. Heath, D., Kasif, S., and Salzberg, S., k-DT: A multi-tree learning method. Proc. Second Int. Workshop Multistrategy Learn. pp. 138-149, 1993.

  13. Heath, D., Kasif, S., and Salzberg, S., Learning oblique decision trees. Proc. Thirteenth Int. Joint Conf. Artif. Intellig. (IJCAI-93) pp. 1002-1007, 1993.

  14. Rich, E., and Knight, K., Artificial Intelligence (2nd edn.), McGraw Hill, New York, 1991.

    Google Scholar 

  15. Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P., Optimization by simulated annealing. Science 220:4598, 1983.

    Google Scholar 

  16. Utgoff, P. E., Incremental induction of decision trees. Mach. Learn. 4(2):161-186, 1989.

    Google Scholar 

  17. Crawford, S., Extensions to the CART algorithm. Int. J. Man-Mach. Stud. 31(2):197-217, 1989.

    Google Scholar 

  18. Dietterich, T. G., and Kong, E. B., Machine learning bias, statistical bias and statistical variance of decision tree algorithms. Technical Report, Oregon State University, 1995.

  19. Ho, T. K., The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intellig. 20(8):832-844, 1998.

    Google Scholar 

  20. Podgorelec, V., and Kokol, P., Evolutionary decision forests-decision making with multiple evolutionary constructed decision trees, Problems in Applied Mathematics and Computational Intelligence, pp. 97-103, WSES Press, 2001.

  21. Shlien, S., Multiple binary decision tree classifiers. Pattern Recogn. Lett. 23(7):757-763, 1992.

    Google Scholar 

  22. Utgoff, P. E., Perceptron trees: A case study in hybrid concept representations. Connect. Science 1:377-391, 1989.

    Google Scholar 

  23. Craven, M.W., and Shavlik, J.W., Extracting tree-structured representations of trained networks. In Advances in Neural Information Processing Systems, Vol. 8, MIT Press, Cambridge, MA, 1996.

    Google Scholar 

  24. Zorman, M., Kokol, P., and Podgorelec, V., Medical decision making supported by hybrid decision trees. Proc. ICSC Symp. Intellig. Syst. Appl. (ISA-2000) ICSC Academic Press, 2000.

  25. Banerjee, A., Initializing neural networks using decision trees. Proc. Int. Workshop Comput. Learn. Nat. Learn. Syst. pp. 3-15, 1994.

  26. Goldberg, D. E., Genetic algorithms in search, optimization, and machine learning, AddisonWesley, Reading, MA, 1989.

    Google Scholar 

  27. Nikolaev, N., and Slavov, V., Inductive genetic programming with decision trees. Intellig. Data Anal. Int. J. 2(1):31-44, 1998.

    Google Scholar 

  28. Podgorelec, V., and Kokol, P., Induction f medical decision trees with genetic algorithms. Proc. Int. ICSC Congr. Comput. Intellig. Methods Appl. (CIMA 1999) 1999.

  29. Cantu-Paz, E., and Kamath, C., Using evolutionary algorithms to induce oblique decision trees. Proc. Genet. Evol. Comput. Conf. (GECCO-2000) pp. 1053-1060, 2000.

  30. Podgorelec, V., and Kokol, P., Towards more optimal medical diagnosing with evolutionary algorithms. J. Med. Syst. 25(3):195-219, 2001.

    Google Scholar 

  31. Sprogar, M., Kokol, P., Hleb, S., Podgorelec, V., and Zorman, M., Vector decision trees. Intellig. Data Anal. 4(3/4):305-321, 2000.

    Google Scholar 

  32. Podgorelec, V., Intelligent Systems Design and Knowledge Discovery With Automatic Programming, PhD thesis, University of Maribor, Oct. 2001.

  33. Cremilleux, B., and Robert, C., A theoretical framework for decision trees in uncertain domains: Application to medical data sets. In Lecture Notes in Artificial Intelligence, Vol. 1211, pp. 145-156, Springer-Verlag, 1997.

    Google Scholar 

  34. Kokol, P., Zorman, M., Stiglic, M. M., and Malcic, I., The limitations of decision trees and automatic learning in real world medical decision making. Proc. 9thWorld Congr. Med. Inform. (MEDINFO-98) Vol. 52, pp. 529-533, 1998.

    Google Scholar 

  35. Tsien, C. L., Fraser, H. S. F., Long, W. J., and Kennedy, R. L., Using classification tree and logistic regression methods to diagnose myocardial infarction. Proc. 9th World Congr. Med. Inform. (MEDINFO-98) Vol. 52, pp. 493-497, 1998.

    Google Scholar 

  36. Babic, S. H., Kokol, P., and Stiglic, M. M., Fuzzy decision trees in the support of breastfeeding. Proc. 13th IEEE Symp. Comp.-Based Med. Syst. (CBMS-2000) pp. 7-11, 2000.

  37. Jones, J. K., The role of data mining technology in the identification of signals of possible adverse drug reactions: Value and limitations. Curr. Ther. Res.-Clin. Exp. 62(9):664-672, 2001.

    Google Scholar 

  38. Ohno-Machado, L., Lacson, R., and Massad, E., Decision trees and fuzzy logic: A comparison of models for the selection of measles vaccination strategies in Brazil. J. Am. Med. Inform. Assoc. (Suppl.):625-629, September 2000.

  39. Dantchev, N., Therapeutic decision frees in psychiatry. Encephale-Revue De Psychiatrie Clinique Biologique Et Therapeutique 22(3):205-214, 1996.

    Google Scholar 

  40. Gambhir, S. S., Decision analysis in nuclear medicine. J. Nucl. Med. 40(9):1570-1581, 1999.

    Google Scholar 

  41. Tsien, C. L., Kohane, I. S., and McIntosh, N., Multiple signal integration by decision tree induction to detect artifacts in the neonatal intensive care unit. Artif. Intellig. Med. 19(3):189-202, 2000.

    Google Scholar 

  42. Bonner, G., Decision making for health care professionals: Use of decision trees within the community mental health setting. J. Adv. Nurs. 35:349-356, 2001.

    Google Scholar 

  43. Letourneau, S., and Jensen, L., Impact of a decision tree on chronic wound care. J. Wound Ostomy Continence Nurs. 25:240-247, 1998.

    Google Scholar 

  44. Sanders, G. D., Hagerty, C. G., Sonnenberg, F. A., Hlatky, M. A., and Owens, D. K., Distributed decision support using a web-based interface: Prevention of sudden cardiac death, Med. Decision Making 19(2):157-166, 2000.

    Google Scholar 

  45. Sims, C. J., Meyn, L., Caruana, R., Rao, R. B., Mitchell, T., and Krohn, M., Predicting cesarean delivery with decision tree models. Am. J. Obstet. Gynecol. 183:1198-1206, 2000.

    Google Scholar 

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Authors and Affiliations

  1. University of Maribor – FERI, Smetanova 17, SI-2000, Maribor, Slovenia

    Vili Podgorelec, Peter Kokol, Bruno Stiglic & Ivan Rozman

Authors
  1. Vili Podgorelec
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  2. Peter Kokol
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  3. Bruno Stiglic
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  4. Ivan Rozman
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Correspondence to Vili Podgorelec.

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Podgorelec, V., Kokol, P., Stiglic, B. et al. Decision Trees: An Overview and Their Use in Medicine. Journal of Medical Systems 26, 445–463 (2002). https://doi.org/10.1023/A:1016409317640

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  • DOI: https://doi.org/10.1023/A:1016409317640

  • decision trees
  • classification
  • decision making
  • machine learning