Abstract
To evaluate some intelligent method for decision-making we need to compare the method against the competing methods to get some idea of its performance and capabilities. In everyday practice this is enough and the method, if proven good, can be used for different problems. In medicine however we have a demand for the best solution in all circumstances. It is therefore impossible to declare one method as acceptable for every type or even every single problem. In this article we would like to stress some important aspects of machine learning in medicine, especially the creation of specific decision models. We believe the evolutionary concept is a good approach to this as it creates many diverse solutions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A Second Opinion Medical, An Information & Physician Verification Service: http://www.physicians-background.com (jan. 2001)
Banzhaf, W., Nordin, P., Keller, R. E., Francone, F. D.: Genetic Programming — An Introduction. Morgan Kaufmann Publishers Inc (1998)
Boudoulas, H., Kolibash, A. J., Baker, P., King, B. D., Wooley, C. F.: Mitral Valve Prolapse and the Mitral Valve Prolapse Syndrome: A Diagnostic Classification and Pathogenesis of Symptoms. American Heart Journal, 118 (1989), 796–818
Kokol, P. Et al: Decision Trees and Automatic Learning and their Use in Cardiology. Journal of Medical Systems. 19(4) (1994)
Podgorelec, V., Kokol, P.: Self-Adaptation of Evolutionary Constructed Decision Trees by Information Spreading. Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms. Springer Verlag (1999) 294–301
Quinlan, J. R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA (1993)
UCI Machine Learning: http://www.ics.uci.edu/~mlearn/Machine-Learning.html (feb. 2001)
Wolpert, D., Macready, W.: No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation, Vol. 1(1) (1997) 67–82
Yale-New Haven Hospital: Getting a Good Second Opinion. http://www.ynhh.org (2001)
Šprogar, M., Kokol, P., Hleb Babič, Š., Podgorelec, V., Zorman, M.: Vector Decision trees. Intelligent Data Analysis 4, IOS Press (2000) 305–321
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Šprogar, M., Kokol, P., Zorman, M., Podgorelec, V., Lhotska, L., Klema, J. (2001). Notes on Medical Decision Model Creation. In: Crespo, J., Maojo, V., Martin, F. (eds) Medical Data Analysis. ISMDA 2001. Lecture Notes in Computer Science, vol 2199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45497-7_41
Download citation
DOI: https://doi.org/10.1007/3-540-45497-7_41
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42734-6
Online ISBN: 978-3-540-45497-7
eBook Packages: Springer Book Archive