Skip to main content
Log in

Evolution in Medical Decision Making

  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The classical approach to medical decision making can be limited by the underlying theories. The evolutionary computation is a different concept, which can find many different solutions of the problem. In medicine, this is useful because of different expectations the decision system must face. We implemented a tool for genetic induction of vector decision trees, which are a good choice for a medical decision model because of their simplicity and transparency. The vector decision tree gives multiple classifications in one single pass. Evolutionary development of such trees achieved good results when the results were statistically compared to those of other classical methods. For medical interpretation however a cooperation with doctors is needed to verify the model build.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

REFERENCES

  1. Kokol, P., Hleb, Š., Podgorelec, V., and Zorman, M., Some ideas about intelligent medical system design, 12th IEEE Symp. Comput.-Based Med. Syst., IEEE Computer Society, Stamford, CT, pp. 48-52, 1999.

    Google Scholar 

  2. Exclusive ore. Retrieved from http:nnwww.xor.com

  3. Podgorelec, V., and Kokol, P., Self-adapting evolutionary decision support model. Proc. 1999 IEEE Int. Symp. Ind. Electron. ISIE'99, IEEE Press, NY, pp. 1484-1489, 1999.

    Google Scholar 

  4. Kokol, P., et al., Participative design, decision trees, automatic learning and medical decision making, Medical Informatics Europe '96, Studies in Health Technology and Informatics, Vol. 34, pp. A501-A505, Amsterdam, 1996.

    Google Scholar 

  5. Quinlan, J. R., Decision trees and instance based classifiers, Artif. Intellig. Robot. 521-535, 1997.

  6. Holland, J., Adaptation in Natural and Artificial Systems, The University of Michigan Press, 1975.

  7. Koza, J. R., Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.

    Google Scholar 

  8. Koza, J., Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press, Cambridge, MA, 1994.

    Google Scholar 

  9. Takagi, H., Introduction to fuzzy systems, neural networks, and genetic algorithms. In Ruan, D. (ed.), Intelligent Hybrid Systems, Kluwer Academic, Norwell, MA, pp. 3-34, 1997.

    Google Scholar 

  10. Bäck, T., Evolutionary Algorithms in Theory and Practice, Oxford University Press, London, 1996.

    Google Scholar 

  11. Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, 1989.

    Google Scholar 

  12. Banzhaf, W., Nordin, P., Keller, R. E., and Francone, F. D., Genetic Programming-An Introduction, Morgan Kaufmann, San Mateo, CA, 1998.

    Google Scholar 

  13. Kokol, P., Podgorelec, V., and Malčič, I., Diagnostic process optimization with evolutionary programming. Proc. 11th IEEE Symp. Comput.-Based Med. Syst. CBMS'98, Lubbock, TX, pp. 62-67, June 1998.

  14. Ohguri, H., Ogawa, K., Maeda, T., Maeda, A., and Maruyama, I., Cancer-associated retinopathy induced by both anti-recoverin and anti-hsc70 antibodies in vivo. Invest. Ophthalmol. Vis. Sci. 40(13):3160-3167, 1999.

    Google Scholar 

  15. Andreassi, L., et al., Digital dermoscopy analysis for the differentiation of atypical nevi and early melanoma: A new quantitative simeology. Arch Dermatol. 135(12):1459-1465, 1999.

    Google Scholar 

  16. Fischer, W., and Lamm, D., Experience with radical surgery for tumor persistence or recurrence after primary irradiation of cervix neoplasms. Zentralbl. Gynakol. 97(20):1255-1259, 1975.

    Google Scholar 

  17. RuleQuest. Retrieved from http:nnwww.rulequest.com

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Matej Šprogar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Šprogar, M., Lenič, M. & Alayon, S. Evolution in Medical Decision Making. Journal of Medical Systems 26, 479–489 (2002). https://doi.org/10.1023/A:1016413418549

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1016413418549

Navigation