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Open Medicine

formerly Central European Journal of Medicine

Editor-in-Chief: Darzynkiewicz, Zbigniew


IMPACT FACTOR 2018: 1.221

CiteScore 2018: 1.01

SCImago Journal Rank (SJR) 2018: 0.329
Source Normalized Impact per Paper (SNIP) 2018: 0.479

ICV 2017: 152.94

Open Access
Online
ISSN
2391-5463
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Volume 7, Issue 2

Issues

Volume 10 (2015)

Different decision tree induction strategies for a medical decision problem

Robert Burduk
  • Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology Wybrzeze, Wyspianskiego 27, 50-370, Wroclaw, Poland
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/ Michal Wozniak
  • Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology Wybrzeze, Wyspianskiego 27, 50-370, Wroclaw, Poland
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Published Online: 2012-02-03 | DOI: https://doi.org/10.2478/s11536-011-0142-x

Abstract

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.

Keywords: Acute abdominal pain; Univariate and multivariate decision trees; Bayes decision theory; Multistage classifier; Medical decision support systems

  • [1] Liebowitz J. [ed], The Handbook of Applied Expert Systems, CRC Press, 1998 Google Scholar

  • [2] Shortliffe E., MYCIN: Computer-based Medical Consultations, New York: American Elsivier, 1975 Google Scholar

  • [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 http://dx.doi.org/10.1136/jamia.2001.0080527CrossrefGoogle Scholar

  • [4] Kaplan B., Evaluating informatics applications — clinical decision support systems literature review, International Journal of Medical Informatics, 2001, 64, 15–37 http://dx.doi.org/10.1016/S1386-5056(01)00183-6CrossrefGoogle Scholar

  • [5] Mextaxiotis K., Samouilidis J.E., Expert systems in medicine: academic illusion or real power? Information Management & Security, 2000, 75–79 Google Scholar

  • [6] Wozniak M., Two-Stage Classifier for Diagnosis of Hypertension Type, LNCS, 2006, 4345, 433–440 Google Scholar

  • [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 Google Scholar

  • [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 http://dx.doi.org/10.2478/s11536-009-0063-0Web of ScienceCrossrefGoogle Scholar

  • [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 http://dx.doi.org/10.1016/j.dsp.2006.04.007CrossrefGoogle Scholar

  • [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 Google Scholar

  • [11] Übeyli E.D., Comparison of different classification algorithms in clinical decision-making Expert Systems, 2007, 24(1), 17–31 Google Scholar

  • [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 http://dx.doi.org/10.2478/s11536-007-0041-3Web of ScienceCrossrefGoogle Scholar

  • [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 http://dx.doi.org/10.2478/s11536-009-0059-9CrossrefGoogle Scholar

  • [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 http://dx.doi.org/10.2478/s11536-008-0085-zCrossrefWeb of ScienceGoogle Scholar

  • [15] Safavian, S.R., Landgrebe, D., A survey of decision tree classifier methodology. IEEE Trans. Systems, Man Cyber., 1991, 21(3), 660–674 http://dx.doi.org/10.1109/21.97458CrossrefGoogle Scholar

  • [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 Google Scholar

  • [17] Duda R.O., Hart P.E., Stork D.G., Pattern Classification, John Wiley and Sons, 2000 Google Scholar

  • [18] Devijver P. A., Kittler J., Pattern Recognition: A Statistical Approach, Prentice Hall, London, 1982 Google Scholar

  • [19] Burduk R., Kurzyński M., Two-stage binary classifier with fuzzy-valued loss function, Pattern Analysis and Applications, 2006, 9(4), 353–358 http://dx.doi.org/10.1007/s10044-006-0043-9CrossrefGoogle Scholar

  • [20] Alpaydin E., Introduction to Machine Learning. Second edition, The MIT Press, Cambridge, MA, USA, London, UK, 2010 Google Scholar

  • [21] Mitchell T.M., Machine Learning, McGraw-Hill Comp., Inc, New York, 1997 Google Scholar

  • [22] Quinlan J.R., Induction on Decision Tree, Machine Learning, 1986, 1, 81–106 CrossrefGoogle Scholar

  • [23] Quinlan J.R., C4.5: Program for Machine Learning, Morgan Kaufman, San Mateo, CA, 1993 Google Scholar

  • [24] Breiman L., Friedman J.H., Olshen R.A., Stone C.J., Classification and Decision trees, Belmont, CA, Wadsworth, 1984 Google Scholar

  • [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 Google Scholar

  • [26] Brodley C.E., Utgoff P.E., Multivariate Decision Trees, Machine Learning, 1995, 19(1), 45–77 CrossrefWeb of ScienceGoogle Scholar

  • [27] Kurzynski M., The optimal strategy of a tree classifier, Pattern Recognition, 1983, 16(1), 81–87 http://dx.doi.org/10.1016/0031-3203(83)90011-0CrossrefGoogle Scholar

  • [28] Landwehr N., Hall M., Frank E., Logistic model trees, LNCS, 2003, 2837, 241–252 Google Scholar

  • [29] Blum A.L., Langley P., Selection of Relevant Features and Examples in Machine Learning, Artificial Intelligence, 1997, 97(1–2), 245–271 http://dx.doi.org/10.1016/S0004-3702(97)00063-5CrossrefGoogle Scholar

  • [30] Dash M., Liu H., Feature Selection for Classification, Intelligent Data Analysis, 1997, 1(1–4), 131–156 http://dx.doi.org/10.1016/S1088-467X(97)00008-5CrossrefGoogle Scholar

  • [31] Kurzynski M., Diagnosis of acute abdominal pain using three-stage classifier, Computers in Biology and Medicine, 1987, 17(1), 19–27 http://dx.doi.org/10.1016/0010-4825(87)90030-8CrossrefGoogle Scholar

  • [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 Google Scholar

  • [33] Shi H., Best-first decision tree learning. Hamilton, NZ, 2007 Google Scholar

  • [34] Holmes G., Pfahringer B., Kirkby R., Frank E., Hall E., Multiclass alternating decision trees. Proceedings of ECML, 2001, 161–172 Google Scholar

  • [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. Google Scholar

  • [36] Gama J., Functional Trees, Machine Learning, 2004, 55(3), 219–250 CrossrefGoogle Scholar

  • [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 Google Scholar

  • [38] Witten I.H., Frank E., Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Pub, 2000 Google Scholar

  • [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 Google Scholar

  • [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 Google Scholar

  • [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 Google Scholar

About the article

Published Online: 2012-02-03

Published in Print: 2012-04-01


Citation Information: Open Medicine, Volume 7, Issue 2, Pages 183–193, ISSN (Online) 2391-5463, DOI: https://doi.org/10.2478/s11536-011-0142-x.

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