A data pre-processing method to increase efficiency and accuracy in data mining

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3581 LNAI, Issue , 2005, Pages 434-443

  Razavi, Amir R ^a   Gill, Hans ^a   Åhlfeldt, Hans ^a   Shahsavar, Nosrat ^a,b  

^a LINKÖPING UNIVERSITY   (Sweden)

^b LINKÖPING UNIVERSITY HOSPITAL   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

CORRELATION METHODS; DATA REDUCTION; DECISION THEORY; MATHEMATICAL MODELS; PATIENT MONITORING;

CANCER PATIENTS; CANONICAL CORRELATION ANALYSIS (CCA); DECISION TREE INDUCTION (DTI); EXPECTATION MAXIMIZATION ALGORITHM;

PROGRAM PROCESSORS;

EID: 26944458835     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11527770_59     Document Type: Conference Paper

References (29)

1. Uckert, F., Ataian, M., Gorz, M., Prokosch, H. U.: Functions of an electronic health record. Int J Comput Dent 5 (2002) 125-32
2. Sandblom, G., Dufmats, M., Nordenskjold, K., Varenhorst, E.: Prostate carcinoma trends in three counties in Sweden 1987-1996: results from a population-based national cancer register. South-East Region Prostate Cancer Group. Cancer 88 (2000) 1445-53
3. Rosen, M.: National Health Data Registers; a Nordic heritage to public health. Scand J Public Health 30 (2002)81-5
4. Windle, P. E.: Data mining: an excellent research tool. J Perianesth Nurs 19 (2004) 355-6
5. Quinlan, J. R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA (1993)
6. Podgorelec, V., Kokol, P., Stiglic, B., Rozman, I.: Decision trees: an overview and their use in medicine. J Med Syst 26 (2002) 445-63
7. Vlahou, A., Schorge, J. O., Gregory, B. W., Coleman, R. L.: Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data. J Biomed Biotechnol 2003 (2003) 308-314
8. Gerald, L. B., Tang, S., Bruce, F., Redden, D., Kimerling, M. E., Brook, N., Dunlap, N., Bailey, W. C.: A decision tree for tuberculosis contact investigation. Am J Respir Crit Care Med 166 (2002) 1122-7
9. Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann (2001)
10. Duhamel, A., Nuttens, M. C., Devos, P., Picavet, M., Beuscart, R.: A preprocessing method for improving data mining techniques. Application to a large medical diabetes database. Stud Health Technol Inform 95 (2003) 269-74
11. McLachlan, G. J., Krishnan, T.: The EM algorithm and extensions. John Wiley & Sons (1997)
12. Silva Cardoso, E., Blalock, K., Allen, C. A., Chan, F., Rubin, S. E.: Life skills and subjective well-being of people with disabilities: a canonical correlation analysis. Int J Rehabil Res 27 (2004) 331-4
13. Antoniadis, A., Lambert-Lacroix, S., Leblanc, F.: Effective dimension reduction methods for tumor classification using gene expression data. Bioinformatics 19 (2003) 563-70
14. Dempster, A. P., Laird, N. M., Rubin, D. B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. J R Stat Soc Ser B 39 (1977) 1-38
15. Vogel, R. L., Ackermann, R. J.: Is primary care physician supply correlated with health outcomes? Int J Health Serv 28 (1998) 183-96
16. Dunlap, W., Landis, R.: Interpretations of multiple regression borrowed from factor analysis and canonical correlation. J Gen Psychol 125 (1998) 397-407
17. Thompson, B.: Canonical correlation analysis: Uses and interpretation. Sage, Thousand Oaks, CA (1984)
18. SPSS Inc.: SPSS for Windows. SPSS Inc. (2001)
19. Pavlopoulos, S. A., Stasis, A. C., Loukis, E. N.: A decision tree-based method for the differential diagnosis of Aortic Stenosis from Mitral Regurgitation using heart sounds. Biomed Eng Online 3 (2004) 21
20. Luo, Y., Lin, S.: Information gain for genetic parameter estimation with incorporation of marker data. Biometrics 59 (2003) 393-401
21. Zorman, M., Eich, H. P., Stiglic, B., Ohmann, C., Lenic, M.: Does size really matter-using a decision tree approach for comparison of three different databases from the medical field of acute appendicitis. J Med Syst 26 (2002) 465-77
22. Esposito, F., Malerba, D., Semeraro, G., Kay, J.: A comparative analysis of methods for pruning decision trees. IEEE Trans Pattern Anal Mach Intell 19 (1997) 476-491
23. Witten, I. H., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)
24. Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proc. International Joint Conference on Artificial Intelligence (1995) 1137-1145
25. Delen, D., Walker, G., Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med In press (2004)
26. Pfaff, M., Weller, K., Woetzel, D., Guthke, R., Schroeder, K., Stein, G., Pohlmeier, R., Vienken, J.: Prediction of cardiovascular risk in hemodialysis patients by data mining. Methods Inf Med 43 (2004)106-113
27. Babic, A.: Knowledge discovery for advanced clinical data management and analysis. Stud Health Technol Inform 68 (1999) 409-13
28. Cios, K. J., Moore, G. W.: Uniqueness of medical data mining. Artif Intell Med 26 (2002) 1-24
29. Myrtveit, I., Stensrud, E., Olsson, U. H.: Analyzing data sets with missing data: an empirical evaluation of imputation methods and likelihood-based methods. IEEE Trans Soft w Eng 27 (2001) 999-1013