Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction

Artificial Intelligence in Medicine

Volumn 52, Issue 3, 2011, Pages 123-139

  Gagliardi, Francesco ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Bias variance dilemma; Diabetes mellitus; Erythemato squamous diseases; Instance based learning; Knowledge discovery in databases; Nearest neighbour rule; Nosological diagnosis; Prototype methods; Single proton emission computed tomography

Indexed keywords

BIAS-VARIANCE DILEMMA; DIABETES MELLITUS; ERYTHEMATO-SQUAMOUS DISEASE; INSTANCE BASED LEARNING; KNOWLEDGE DISCOVERY IN DATABASES; NEAREST-NEIGHBOUR; NOSOLOGICAL DIAGNOSIS; PROTOTYPE METHODS; SINGLE PROTON EMISSION COMPUTED TOMOGRAPHIES;

ARTIFICIAL INTELLIGENCE; COMPUTERIZED TOMOGRAPHY; DATABASE SYSTEMS; DECISION SUPPORT SYSTEMS; DIAGNOSTIC PRODUCTS; MEDICAL COMPUTING; MEDICAL EDUCATION; OPTIMIZATION; TOMOGRAPHY;

DIAGNOSIS;

ACCURACY; ARTICLE; CARDIOLOGY; CLASSIFIER; DIABETES MELLITUS; DIAGNOSTIC IMAGING; DIFFERENTIAL DIAGNOSIS; ERYTHEMATOSQUAMOUS SKIN DISEASE; FEASIBILITY STUDY; KNOWLEDGE DISCOVERY; MEDICAL INFORMATION SYSTEM; PERFORMANCE; PRIORITY JOURNAL; REFERENCE DATABASE; SENSITIVITY AND SPECIFICITY; SYSTEM ANALYSIS; VALIDATION PROCESS;

DATABASES, FACTUAL; DIAGNOSIS; HUMANS; KNOWLEDGE;

EID: 80053012604     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2011.04.002     Document Type: Article

References (48)

1. Witten I.H., Frank E. Data mining: practical machine learning tools and techniques with Java implementations 2005, San Francisco, CA, Morgan Kaufmann. 2nd ed.
2. Machine learning, neural and statistical classification 1994, Prentice Hall, Englewood, NJ. D. Michie, D.J. Spiegelhalter, C.C. Taylor (Eds.).
3. Duda R., Hart P., Stork D. Pattern classification 2000, John Wiley & Sons, New York, NY. 2nd ed.
4. Coiera E. Guide to health informatics 2003, Arnold Publishers, London. 2nd ed.
5. Cios K.J., Pedrycz W., Swiniarski R. Data mining methods for knowledge discovery 1998, Springer, Heidelberg.
6. Cios K.J., Moore G.W. Medical data mining and knowledge discovery: overview of key issues. Medical data mining and knowledge discovery 2001, 1-16. Springer, Heidelberg. K.J. Cios (Ed.).
7. Fayyad U., Piatetsky-Shapiro G., Smyth P., Uturusamy R. Advances in knowledge discovery and data mining 1996, The MIT Press, Cambridge, MA.
8. Cios K.J., Moore G.W. Uniqueness of medical data mining. Artif Intell Med 2002, 26(1-2):1-24. 10.1016/S0933-3657(02)00049-0.
9. Aha D.W., Kibler D., Albert M.K. Instance-based learning algorithms. Mach Learn 1991, 6:37-66. 10.1023/A:1022689900470.
10. Cover TM, Hart PE. Nearest pattern classification. IEEE Trans Inform Theory IT-13(1):21-7; 1967 [accessed 29.07.10.]. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1053964.
11. Wilson D.R., Martinez T.R. Reduction techniques for exemplar-based learning algorithms. Mach. Learn. 2000, 38(3):257-268. 10.1023/A:1007626913721.
12. Gottrup C., Thomsen K., Locht P., Wu O., Sorensen A.G., Koroshetz W.J., et al. Applying instance-based techniques to prediction of final outcome in acute stroke. Artif Intell Med 2005, 33(3):223-236. 10.1016/j.artmed.2004.06.003.
13. Lee M.C., Nelson S.J. Supervised pattern recognition for the prediction of contrast-enhancement appearance in brain tumors from multivariate magnetic resonance imaging and spectroscopy. Artif Intell Med 2008, 43(1):61-74. 10.1016/j.artmed.2008.03.002.
14. Latifoglu F., Polat K., Kara S., Günes S. Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS). J Biomed Inform 2008, 41(1):15-23. 10.1016/j.jbi.2007.04.001.
15. Wyns B., Sette S., Boullart L., Baeten D., Hoffman I., De Keyser F. Prediction of diagnosis in patients with early arthritis using a combined Kohonen mapping and instance-based evaluation criterion. Artif Intell Med 2004, 31(1):45-55. 10.1016/j.artmed.2004.01.002.
16. Schleif F.M., Villmann T., Hammer B. Prototype based fuzzy classification in clinical proteomics. Int J Approx Reason 2008, 47(1):4-16. 10.1016/j.ijar.2007.03.005.
17. Cebron N., Berthold M.R. Adaptive prototype-based fuzzy classification. Fuzzy Sets Syst 2008, 159(21):2806-2818. 10.1016/j.fss.2008.03.019.
18. Rasheed S.M., Stashuk D.W., Kamel M. A software package for interactive motor unit potential classification using fuzzy k-NN classifier. Comput Methods Programs Biomed 2008, 89(1):56-71. 10.1016/j.cmpb.2007.10.006.
19. Wilson D.R., Martinez T.R. Improved heterogeneous distance functions. J Artif Intell Res 1997, 6:1-34. 10.1613/jair.346.
20. Kuncheva L.I., Bezdek J.C. Nearest prototype classification: clustering, genetic algorithms, or random search?. IEEE Trans Syst Man Cybern Part C 1998, 28(1):160-164. 10.1109/5326.661099.
21. Bezdek J.C., Reichherzer T.R., Lim G.S., Attikiouzel Y. Multiple prototype classifier design. IEEE Trans Syst Man Cybern Part C 1998, 28(1):67-79. 10.1109/5326.661091.
22. Henery R.J. Methods for comparison. Machine learning, neural and statistical classification 1994, 107-124. Prentice Hall, Englewood NJ. D. Michie, D.J. Spiegelhalter, C.C. Taylor (Eds.).
23. Nieddu L., Patrizi G. Formal methods in pattern recognition: a review. Eur J Oper Res 2000, 120:459-495. 10.1016/S0377-2217(98)00368-3.
24. Hastie T., Tibshirani R., Friedman J. The elements of statistical learning. Data mining, inference, and prediction 2009, Springer, New York, NY, doi:10.1007/b94608. 2nd edn.
25. Webb A.R. Statistical pattern recognition 2002, John Wiley & Sons, Chichester, UK, doi:10.1002/0470854774. 2nd edn.
26. Veenman C.J., Reinders M.J.T. The nearest subclass classifier: a compromise between the nearest mean and nearest neighbor classifier. IEEE Trans Pattern Anal Mach Intell 2005, 27(9):1417-1429. doi:10.1109/TPAMI.2005.187.
27. Kurgan L.A., Cios K.J., Tadeusiewicze R., Ogielae M., Goodendayf L.S. Knowledge discovery approach to automated cardiac SPECT diagnosis. Artif Intell Med 2001, 23(2):149-169. 10.1016/S0933-3657(01)00082-3.
28. Sadegh-Zadeh K. Fundamentals of clinical methodology: 4. Diagnosis. Artif Intell Med 2000, 20:227-241. 10.1016/S0933-3657(00)00066-X.
29. Scandellari C. La strategia della diagnosi 1981, Piccin editore, Padova, Italy.
30. Gioriello G., Moriggi S. Tra diagnosi e scoperta. Una rilettura del caso Semmelweis. Forma della Razionalità Medica. Soveria Mannelli (CZ) 2004, 9-30. Rubbettino Scientifica, Italy. G. Federspil, P. Giaretta (Eds.).
31. Sadegh-Zadeh K. Fundamentals of clinical methodology: 3. Nosology. Artif Intell Med 1999, 17:87-108. 10.1016/S0933-3657(99)00015-9.
32. Hofmann B. Complexity of the concept of disease as shown through rival theoretical frameworks. Theor Med Bioet 2001, 22(3):211-236. 10.1023/A:1011416302494.
33. Sadegh-Zadeh K. Fuzzy health, illness, and disease. J Med Philos 2000, 25(5):605-638. 10.1076/0360-5310(200010)25:5;1-W;FT605.
34. D'Amico R Is disease a natural kind?. J Med Philos 1995, 20(5):551-569. 10.1093/jmp/20.5.551.
35. Guvenir H.A., Demiroz G., Ilter N. Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artif Intell Med 1998, 13:147-165. 10.1016/S0933-3657(98)00028-1.
36. Asuncion A, Newman DJ. UCI machine learning repository. Irvine, CA: University of California, School of Information and Computer Science; 2007 [accessed 03.06.09.]. http://archive.ics.uci.edu/ml/.
37. Breault J.L., Goodall C.R., Fos P.J. Data mining a diabetic data warehouse. Artif Intell Med 2002, 26(1-2):37-54. 10.1016/S0933-3657(02)00051-9.
38. Smith J.W., Everhart J.E., Dickson W.C., Knowler W.C., Johannes R.S. Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. Proceedings of the the twelfth annual symposium on computer applications and medical care 1988, 261-265. IEEE Computer Society Press, Los Angles, CA. R.A. Greenes (Ed.).
39. Breault J.L. Data mining diabetic databases: are rough sets a useful addition?. Computing science and statistics 2001, 33:51-60. Interface Foundation of North America, Fairfax Station, VA.
40. Pearson RK, The problem of disguised missing data. SIGKDD Explorat 8(1): 83-92; 2006 [accessed 04.06.09.]. http://kdd.org/explorations/issues/8-1-2006-06/12-Pearson.pdf.
41. DesJardins D. Outliers, inliers, and just plain liars: new EDA+ techniques for understanding data. Proceedings of the twenty-sixth annual SAS® users group international conference 2001, SAS Institute Inc., Cary, NC, [Paper 169]. F. Fry (Ed.).
42. Lachenbruch P., Mickey R. Estimation of error rates in discriminant analysis. Technometrics 1968, 10:1-11.
43. Hand D.J. Construction and assessment of classification rules 1997, John Wiley & Sons, New York, NY.
44. Friedman J.H. On bias, variance, 0/1-loss, and the curse-of-dimensionality. Data Min Knowl Discov 1997, 1(1):55-77. 10.1023/A:1009778005914.
45. Revett K, Gorunescu F, Salem AB, El-Dahshan ES. Evaluation of the feature space of an erythematosquamous dataset using rough sets. Ann Univers Craiova-Math Comput Sci Ser 36(2): 123-130; 2009 [accessed 01.07.10.]. http://inf.ucv.ro/~ami/index.php/ami/article/view/294/285.
46. Reunanen J. Overfitting in making comparisons between variable selection methods. J Mach Learn Res 2003, 3:1371-1382. http://jmlr.csail.mit.edu/papers/v3/reunanen03a.html [accessed 09.07.10.].
47. Caruana R., de Sa V.R. Benefitting from the variables that variable selection discards. J Mach Learn Res 2003, 3:1245-1264. http://jmlr.csail.mit.edu/papers/v3/caruana03a.html [accessed 09.07.10.].
48. Hesslow G. Do we need a concept of disease?. Theor Med 1993, 14:1-14.