Modern parameterization and explanation techniques in diagnostic decision support system: A case study in diagnostics of coronary artery disease

Artificial Intelligence in Medicine

Volumn 52, Issue 2, 2011, Pages 77-90

  Kukar, Matjaž ^a   Kononenko, Igor ^a   Grošelj, Ciril ^b  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

^b UNIVERSITY MEDICAL CENTRE LJUBLJANA   (Slovenia)

Author keywords

Association rules; Coronary artery disease diagnostics; Machine learning; Multi resolution image parameterization; Principal component analysis

Indexed keywords

AUTOMATIC CLASSIFIERS; BUILDING MACHINES; CLINICAL PRACTICES; CLINICAL SETTINGS; CORONARY ANGIOGRAPHY; CORONARY ARTERY DISEASE; DIAGNOSTIC ACCURACY; DIAGNOSTIC DECISIONS; DIAGNOSTIC PERFORMANCE; DIAGNOSTIC PROBLEM; DIAGNOSTIC PROCEDURE; DIAGNOSTIC QUALITY; EARLY DIAGNOSIS; GOLD STANDARDS; IMAGE PARAMETERS; INEXPENSIVE MEANS; MACHINE LEARNING METHODS; MACHINE-LEARNING; MEDICAL IMAGES; MULTIPLE RESOLUTIONS; MULTIRESOLUTION IMAGES; MYOCARDIAL PERFUSION; NON-INVASIVE; PARAMETERIZED; PRINCIPAL COMPONENTS; REFERENCE METHOD; SCINTIGRAPHIC IMAGES; SYNTHETIC DATASETS; TEXTURE DESCRIPTION;

ARTIFICIAL INTELLIGENCE; ASSOCIATION RULES; DECISION SUPPORT SYSTEMS; DISEASES; HEART; LEARNING SYSTEMS; MEDICAL IMAGING; NUCLEAR MEDICINE; PARAMETERIZATION; PRINCIPAL COMPONENT ANALYSIS;

DIAGNOSIS;

ADULT; ARTICLE; CASE STUDY; CONTROLLED STUDY; CORONARY ARTERY DISEASE; DECISION SUPPORT SYSTEM; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; FEMALE; HUMAN; IMAGE ANALYSIS; IMAGE PARAMETERIZATION; IMAGE PROCESSING; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MYOCARDIAL PERFUSION IMAGING; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY;

ARTIFICIAL INTELLIGENCE; CORONARY ANGIOGRAPHY; CORONARY ARTERY DISEASE; DECISION SUPPORT SYSTEMS, CLINICAL; HEART; HUMANS; MYOCARDIAL PERFUSION IMAGING; PRINCIPAL COMPONENT ANALYSIS;

EID: 79959744776     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2011.04.009     Document Type: Article

References (59)

1. Lowe D.G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 2004, 60(2):91-110.
2. Mikolajczyk K., Tuytelaars T., Schmid C., Zisserman A., Matas J., Schaffalitzky F., Kadir T., Gool L.V. A comparison of affine region detectors. International Journal of Compututer Vision 2005, 65:43-72.
3. Bay H., Ess A., Tuytelaars T., Gool L.V. SURF: speeded up robust features. Computer Vision and Image Understanding 2008, 110:346-359.
4. Fitzpatrick J., Sonka M. Handbook of medical imaging, medical image processing and analysis, vol. 2 2000, SPIE, Bellingham.
5. Olona-Cabases M. The probability of a correct diagnosis. Nuclear cardiology in everyday practice 1994, 348-357. Kluwer Academic Publishers, Dordrecht, The Netherlands. J. Candell-Riera, D. Ortega-Alcalde (Eds.).
6. Pollock B.H. Computer-assisted interpretation of noninvasive tests for diagnosis of coronary artery disease. Cardiovascular Reviews and Reports 1983, 4:367-375.
7. Kukar M., Grošelj C. Machine learning in stepwise diagnostic process. Proc. joint European conference on artificial intelligence in medicine and medical decision making 1999, 315-325. Springer, Berlin, Heidelberg. W. Horn, Y. Shahar, G. Lindberg, S. Andreassen, J. Wyatt (Eds.).
8. Kukar M., Kononenko I., Grošelj C., Kralj K., Fettich J. Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artificial Intelligence in Medicine 1999, 16(1):25-50.
9. Allison J.S., Heo J., Iskandrian A.E. Artificial neural network modeling of stress single-photon emission computed tomographic imaging for detecting extensive coronary artery disease. The American Journal of Cardiology 2005, 95(2):178-181.
10. Mobley B.A., Schechter E., Moore W.E. Predictions of coronary artery stenosis by artificial neural network. Artificial Intelligence in Medicine 2000, 18(3):187-203.
11. Ohlsson M. WeAidU-a decision support system for myocardial perfusion images using artificial neural networks. Artificial Intelligence in Medicine 2004, 30:49-60.
12. Garcia E.V., Cooke C.D., Folks R.D., Santana C.A., Krawczynska E.G., De Braal L., Ezquerra N.F. Diagnostic performance of an expert system for the interpretation of myocardial perfusion spect studies. The Journal of Nuclear Medicine 2001, 42(8):1185-1191.
13. Gamberger D., Lavrač N., Krstačič G. Active subgroup mining: a case study in coronary heart disease risk group detection. Artificial Intelligence in Medicine 2003, 28(1):27-57.
14. Slomka P.J., Nishina H., Berman D.S., Akincioglu C., Abidov A., Friedman J.D., Hayes S.W., Germano G. Automated quantification of myocardial perfusion spect using simplified normal limits. Journal of Nuclear Cardiology 2005, 12(1):66-77.
15. Kurgan L.A., Cios K.J., Tadeusiewicz R. Knowledge discovery approach to automated cardiac spect diagnosis. Artificial Intelligence in Medicine 2001, 23(2):149-169.
16. Vasconcelos N. From pixels to semantic spaces:advances in content-based image retrieval. Computer 2007, 40(7):20-26.
17. Jain A.K., Duin R.P.W., Mao J. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000, 22(1):4-7. 1.
18. Šajn L., Kononenko I. Multiresolution image parametrization for improving texture classification. EURASIP Journal on Advances in Signal Processing 2008, 2008(1):1-12.
19. Nixon M., Aguado A.S. Feature extraction and image processing 2008, Academic Press, Elsevier, Amsterdam. 2nd ed.
20. Šajn L., Kononenko I. Image segmentation and parametrization for automatic diagnostics of whole-body scintigrams. Computational intelligence in medical imaging: techniques & applications 2009, 347-377. CRC Press; Taylor & Francis Group, Boca Raton, London, New York. G. Schaefer, A. Hassanien, J. Jiang (Eds.).
21. Kononenko I., Kukar M. Machine learning and data mining: introduction to principles and algorithms 2007, Horwood Publishing, Chichester, Great Britain.
22. Peng Y., Yao B., Jiang J. Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis. Artificial Intelligence in Medicine 2006, 37(1):43-53.
23. Katsis C.D., Goletsis Y., Likas A., Fotiadis D.I., Sarmas I. A novel method for automated EMG decomposition and MUAP classification. Artificial Intelligence in Medicine 2006, 37(1):55-64.
24. Kukar M., Šajn L., Grošelj C., Grošelj J. Multi-resolution image parametrization in sequential diagnostics of coronary artery disease. Proc. artificial intelligence in medicine 2007, 119-129. Springer, Berlin, Heidelberg. R. Bellazzi, A. Abu-Hanna, J. Hunter (Eds.).
25. Kononenko I. Inductive and Bayesian learning in medical diagnosis. Applied Artificial Intelligence 1993, 7:317-337.
26. Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine 2001, 3:89-109.
27. Haralick R.M., Shanmugam K., Dinstein I. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 1973, 3(11):610-621.
28. Tüceryan M., Jain A.K. Texture segmentation using voronoi polygons. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990, 12(2):211-216.
29. Tüceryan M., Jain A.K. Texture analysis. The handbook of pattern recognition and computer vision 1998, 207-248. World Scientific Publishing Company, New Jersey, USA. 2nd ed. C.H. Chen, L.F. Pau, P.S.P. Wang (Eds.).
30. Rosenfeld A., Kak A.C. Digital picture processing, vol. 2 1982, Academic Press, Inc., New York. 2nd ed.
31. Rushing J.A., Ranganath H.S., Hinke T.H., Graves S.J. Using association rules as texture features. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001, 23(8):845-858.
32. Laws KI. Textured image segmentation. PhD thesis, Dept. Electrical Engineering, University of Southern California, Los Angeles, CA, USA; 1980.
33. Manjunath B.S., Ma W.Y. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996, 18(8):837-842.
34. Agrawal R., Imielinski T., Swami A.N. Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD international conference on management of data 1993, 207-216. ACM Press, New York. P. Buneman, S. Jajodia (Eds.).
35. Bevk M., Kononenko I. Towards symbolic mining of images with association rules: preliminary results on textures. Intelligent Data Analysis 2006, 10(4):379-393.
36. Tüceryan M., Jain A.K. Texture analysis. The handbook of pattern recognition and computer vision 1993, 235-276. World Scientific Publishing Company, New Jersey, USA. C.H. Chen, L.F. Pau, P.S.P. Wang (Eds.).
37. Brin S., Motwani R., Silverstein C. Beyond market baskets: generalizing association rules to correlations. Proceedings of the 1997 ACM SIGMOD international conference on management of data 1997, 265-276. ACM Press, New York, USA. J. Peckham (Ed.).
38. Tan P., Kumar V. Interestingness measures for association patterns: a perspective. Technical report TR00-036 2000, Department of Computer Science, University of Minnesota.
39. Tan P., Kumar V., Srivastava J. Selecting the right interestingness measure for association patterns. Proceedings of the eight ACM SIGKDD international conference on knowledge discovery and data mining 2002, ACM Press, New York, USA.
40. Ferreira C.B.R., Borges D.L. Automated mammogram classification using a multi-resolution pattern recognition approach. SIBGRAPI'01: proceedings of the XIV Brazilian symposium on computer graphics and image processing 2001, 76. IEEE Computer Society, Los Alamitos, CA, USA.
41. Comer M.L., Delp E.J. Segmentation of textured images using a multiresolution gaussian autoregressive model. IEEE Transactions on Image Processing 1999, 8(3):408-420. 3.
42. Ojala T., Pietikainen M., Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24(7):971-987.
43. Chui C.K. An introduction to wavelets 1992, Academic Press, San Diego.
44. Rissanen J. A universal prior for integers and estimation by minimum description length. The Annals of Statistics 1993, 11:416-431.
45. Pearson K. Principal components analysis. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 1901, 559-572.
46. Fisher R.A. The use of multiple measurements in taxonomic problems. Annals of Eugenics 1936, 7:179-188.
47. Venables W.N., Ripley B.D. Modern applied statistics with S 2002, Springer, Berlin, Heidelberg.
48. Grošelj C., Kukar M., Fettich J., Kononenko I. Impact of machine learning to the diagnostic certainty of the patient's group with low coronary artery disease probability. Proc. computer-aided data analysis in medicine 1997, 68-74. Jožef Stefan Institute, Bled, Slovenia. I. Kononenko, T. Urbančič (Eds.).
49. General Electric, ECToolbox protocol operator's guide 2001.
50. Lindahl D., Palmer J., Pettersson J., White T., Lundin A., Edenbrandt L. Scintigraphic diagnosis of coronary artery disease: myocardial bull's-eye images contain the important information. Clinical Physiology 1998, 6(18).
51. Robnik-Šikonja M., Kononenko I. Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning 2003, 53:23-69.
52. Fayyad U.M. Multi-interval discretization of continuous-valued attributes for classification learning. Proc. international joint conferences on artificial intelligence 1993, 1022-1027. Morgan Kaufmann, New York. R. Bajcsy (Ed.).
53. Witten I.H., Frank E. Data mining: practical machine learning tools and techniques 2005, Morgan Kaufmann, San Francisco. 2nd ed.
54. Demšar J, Zupan B, Leban G. Orange: from experimental machine learning to interactive data mining, white paper, ; 2004 [accessed 16.08.09]. http://www.ailab.si/orange.
55. Brodatz P. Textures-a photographic album for artists and designers 1966, Reinhold, Dover, New York.
56. Ojala T., Mäenpää T., Pietikäinen M., Viertola J., Kyllönen J., Huovinen S. Outex-new framework for empirical evaluation of texture analysis algorithms. International conference on pattern recognition, vol. 1 2002, 10701. IEEE Computer Society, Los Alamitos, USA.
57. Sahami M. Learning limited dependence Bayesian classifiers. KDD-96: proceedings of the second international conference on knowledge discovery and data mining 1996, 335-338. AAAI Press, Portland, USA. B. Kuipers, B. Webber (Eds.).
58. Robnik M., Kononenko I. Explaining classifications for individual instances. IEEE Transactions on Knowledge and Data Engineering 2008, 20:589-600.
59. Štrumbelj E., Kononenko I. An efficient explanation of individual classifications using game theory. Journal of Machine Learning Research 2010, 11:1-18.