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2012 | 7 | 2 | 183-193
Tytuł artykułu

Different decision tree induction strategies for a medical decision problem

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Języki publikacji
The paper presents a comparative study of selected recognition methods for the medical decision problem -acute abdominal pain diagnosis. We consider if it is worth using expert knowledge and learning set at the same time. The article shows two groups of decision tree approaches to the problem under consideration. The first does not use expert knowledge and generates classifier only on the basis of learning set. The second approach utilizes expert knowledge for specifying the decision tree structure and learning set for determining mode of decision making in each node based on Bayes decision theory. All classifiers are evaluated on the basis of computer experiments.

Opis fizyczny
  • Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology Wybrzeze, Wyspianskiego 27, 50-370, Wroclaw, Poland,
  • Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology Wybrzeze, Wyspianskiego 27, 50-370, Wroclaw, Poland
  • [1] Liebowitz J. [ed], The Handbook of Applied Expert Systems, CRC Press, 1998
  • [2] Shortliffe E., MYCIN: Computer-based Medical Consultations, New York: American Elsivier, 1975
  • [3] Sim I., et al., Clinical Decision Support Systems for the Practice of Evidence-based Medicine, Journal of the American Medical Informatics Association, 2001, 8(6), 527–534[Crossref]
  • [4] Kaplan B., Evaluating informatics applications - clinical decision support systems literature review, International Journal of Medical Informatics, 2001, 64, 15–37[Crossref]
  • [5] Mextaxiotis K., Samouilidis J.E., Expert systems in medicine: academic illusion or real power? Information Management & Security, 2000, 75–79
  • [6] Wozniak M., Two-Stage Classifier for Diagnosis of Hypertension Type, LNCS, 2006, 4345, 433–440
  • [7] Eich H.P., Ohmann C., Lang K., Decision support in acute abdominal pain using an expert system for different knowledge bases, Proceedings of the 10th IEEE Symposium on Computer-Based Medical Systems, 1997, 2–7
  • [8] Karapandzic V.M., Matic M.D., Pesko P.M., Rankovic V.I. Milicic B.R., Risk assessment in coronary patients undergoing abdominal nonvascular surgery, Central European Journal of Medicine, 2009, 4(4), 459–466[WoS][Crossref]
  • [9] Polat K., Güneşa S., The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythemato-squamous diseases, Digital Signal Processing, 2006, 16(6), 922–930[Crossref]
  • [10] Tang T.I., Zheng G., Huang Y., Shu G., A Comparative Study of Medical Data Classification Methods Based on Decision Tree and System Reconstruction Analysis, Industrial Engineering and Management Systems, 2005, 4(1), 102–108
  • [11] Übeyli E.D., Comparison of different classification algorithms in clinical decision-making Expert Systems, 2007, 24(1), 17–31
  • [12] Zhang Q., Wang J., Wang J., Biweekly CHOP therapy improves therapeutic effect in the non-GCB subtype of diffuse large B-cell lymphoma, Central European Journal of Medicine, 2007, 2(4), 488–498[WoS][Crossref]
  • [13] Nikolov M., Simeonova P., Simeonov V., Chemometrics as an option to assess clinical data from diabetes mellitus type 2 patients, Central European Journal of Medicine, 2009, 4(4), 433–443[Crossref]
  • [14] Papaioannou A., Karamanis G., Rigas I., Spanos T., Roupa Z., Determination and modelling of clinical laboratory data of healthy individuals and patients with end-stage renal failure, Central European Journal of Medicine, 2009, 4(1), 37–48[Crossref][WoS]
  • [15] Safavian, S.R., Landgrebe, D., A survey of decision tree classifier methodology. IEEE Trans. Systems, Man Cyber., 1991, 21(3), 660–674[Crossref]
  • [16] Mui J., Fu K.S., Automated classification of nucleated blood cells using a binary tree classifier, IEEE Trans. Pattern Anal. Mach. Intell., 1980, PAMI-2, 429–443
  • [17] Duda R.O., Hart P.E., Stork D.G., Pattern Classification, John Wiley and Sons, 2000
  • [18] Devijver P. A., Kittler J., Pattern Recognition: A Statistical Approach, Prentice Hall, London, 1982
  • [19] Burduk R., Kurzyński M., Two-stage binary classifier with fuzzy-valued loss function, Pattern Analysis and Applications, 2006, 9(4), 353–358[Crossref]
  • [20] Alpaydin E., Introduction to Machine Learning. Second edition, The MIT Press, Cambridge, MA, USA, London, UK, 2010
  • [21] Mitchell T.M., Machine Learning, McGraw-Hill Comp., Inc, New York, 1997
  • [22] Quinlan J.R., Induction on Decision Tree, Machine Learning, 1986, 1, 81–106 [Crossref]
  • [23] Quinlan J.R., C4.5: Program for Machine Learning, Morgan Kaufman, San Mateo, CA, 1993
  • [24] Breiman L., Friedman J.H., Olshen R.A., Stone C.J., Classification and Decision trees, Belmont, CA, Wadsworth, 1984
  • [25] Cover T.M., The Best Two Independent Measurements are Not the Two Best, IEEE Transactions on Systems, Man and Cybernetics, 1974, SMC-4(1), 116–117
  • [26] Brodley C.E., Utgoff P.E., Multivariate Decision Trees, Machine Learning, 1995, 19(1), 45–77 [Crossref][WoS]
  • [27] Kurzynski M., The optimal strategy of a tree classifier, Pattern Recognition, 1983, 16(1), 81–87[Crossref]
  • [28] Landwehr N., Hall M., Frank E., Logistic model trees, LNCS, 2003, 2837, 241–252
  • [29] Blum A.L., Langley P., Selection of Relevant Features and Examples in Machine Learning, Artificial Intelligence, 1997, 97(1–2), 245–271[Crossref]
  • [30] Dash M., Liu H., Feature Selection for Classification, Intelligent Data Analysis, 1997, 1(1–4), 131–156[Crossref]
  • [31] Kurzynski M., Diagnosis of acute abdominal pain using three-stage classifier, Computers in Biology and Medicine, 1987, 17(1), 19–27[Crossref]
  • [32] Townsend C.M., Beauchamp R.D., Evers B.M., Mattox K.L., Sabiston Textbook of Surgery, 17th ed. St. Louis, Mo: WB Saunders, 2004
  • [33] Shi H., Best-first decision tree learning. Hamilton, NZ, 2007
  • [34] Holmes G., Pfahringer B., Kirkby R., Frank E., Hall E., Multiclass alternating decision trees. Proceedings of ECML, 2001, 161–172
  • [35] Sumner M., Frank E., Hall M., Speeding up Logistic Model Tree Induction, Proc. of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 2005, 675–683.
  • [36] Gama J., Functional Trees, Machine Learning, 2004, 55(3), 219–250 [Crossref]
  • [37] Kohavi R., A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of the 14-th International Joint Conference on Artificial Intelligence, San Mateo, 1995, 1137–1143
  • [38] Witten I.H., Frank E., Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Pub, 2000
  • [39] van der Heijden F., Duin R.P.W., de Ridder D., Tax D.M.J, Classification, parameter estimation and state estimation–an engineering approach using Matlab, John Wiley and Sons, 2004
  • [40] Kohavi R., Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid, Proc of the Second International Conference on Knoledge Discovery and Data Mining, 1996, 202–207
  • [41] Turney P.D., Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm, Journal Artificial Intelligence Research, 1995, 2, 369–409
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