A comparison of logistic regression to decision tree induction in the diagnosis of carpal tunnel syndrome

Computers and Biomedical Research

Volumn 32, Issue 5, 1999, Pages 391-414

  Rudolfer, Stephan M ^a   Paliouras, Georgios ^b   Peers, Ian S ^c  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b INSTITUTE OF NANOSCIENCE AND NANOTECHNOLOGY   (Greece)

^c UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; CARPAL TUNNEL SYNDROME; CONTROLLED STUDY; DECISION THEORY; ELECTROMYOGRAPHY; NERVE CONDUCTION; NONHUMAN; PRIORITY JOURNAL; STATISTICAL MODEL;

EID: 0032757971     PISSN: 00104809     EISSN: None     Source Type: Journal    
DOI: 10.1006/cbmr.1999.1521     Document Type: Article

References (35)

1. Michie D., Spiegelhalter D. J., Taylor C. C. Machine Learning, Neural and Statistical Classification. 1994;Ellis Horwood, New York.
2. Rosenbaum R. B., Ochoa J. L. Carpal Tunnel Syndrome and Other Disorders of the Median Nerve. 1993;Butterworth-Heinemann, Stoneham.
3. Rudolfer S. M. CTSS: An interactive microcomputer program for the clinical screening of carpal tunnel syndrome. I. Clinical aspects. Electromyogr. Clin. Neurophysiol. 28:1988;259-262.
4. Rudolfer S. M. CTSS: An interactive microcomputer program for the clinical screening of carpal tunnel syndrome. II. Statistical and computational aspects. Electromyogr. Clin. Neurophysiol. 30:1992;483-489.
5. Rudolfer S. M., Peers I., Paliouras G. Diagnostic strategies for carpal tunnel syndrome. Abstract Book, Sixth Biennial Conference, European Society for Medical Decision Making. 1996;. p. 65.
6. Long W. J., Griffith J. L., Selker H. P., D'Agostino R. B. A comparison of logistic regression to decision-tree induction in a medical domain. Comput. Biomed. Res. 26:1993;74-97.
7. Beck J. R., Shultz E. K. The use of ROC curves in test performance evaluation. Arch. Pathol. Lab. Med. 110:1986;13-20.
8. Egan J. P. Signal Detection Theory and ROC Curve Analysis. 1975;Academic Press, New York.
9. Quinlan J. R. C4.5: Programs for Machine Learning. 1993;Morgan Kaufmann, San Mateo.
10. Rudolfer S. M., Watson P. C., Lesaffre E. Are ordinal models useful for classification? A revised analysis. J. Stat. Comput. Simul. 52:1995;105-132.
11. SAS/STAT Software: Changes and Enhancements through Release 6.11. 1996;SAS Institute, Inc. Cary. p. 381-490.
12. Jessen H., Paliouras G. Predicting labour force participation of women with the use of statistical and learning classification techniques. European Conference in Non-Linear Econometrics (EC2). 1995.
13. White J. C., Hansen S. R., Johnson R. K. A comparison of EMG procedures in the carpal tunnel syndrome with clinical-EMG correlations. Muscle Nerve. 11:1988;1177-1182.
14. Stevens J. C. AAEM Minimonograph No. 26: The electrodiagnosis of carpal tunnel syndrome. Muscle Nerve. 20:1997;1477-1486.
15. Cestnik G., Kononenko I., Bratko I. Assistant-86: A knowledge-elicitation tool for sophisticated users. Bratko I., Lavrač N. Progress in Machine Learning. 1987;31-45 Sigma Press, Wilmslow.
16. Dattatreya G. R., Kanal L. N. Decision trees in pattern recognition. Kanal L. N., Rosenfeld A. Progress in Pattern Recognition. 1985;189-239 North-Holland, Amsterdam.
17. Breiman L., Friedman J. H., Olshen R. A., Stone C. J. Classification and Regression Trees. 1984;Wadsworth, Pacific Grove.
18. Faubertas R. F., Rodewald L. E., Humiston S. G., Szilagyi P. G. ROC Curves for classification trees. Med. Decision Making. 14:1994;169-174.
19. Crichton N. J., Hinde J. P., Marchini J. Models for diagnosing chest pain: Is CART helpful? Stat. Med. 16:1997;717-727.
20. Quinlan J. R. Learning efficient classification procedures and their application to chess end games. Michalski R. S., Carbonell J. G., Mitchell T. M. Machine Learning: An Artificial Intelligence Approach. 1983;463-482 Morgan Kaufmann, San Mateo.
21. Walker S. H., Duncan D. B. Estimation of the probability of an event as a function of several independent variables. Biometrika. 54:1967;167-179.
22. McCullagh P. Regression models for ordinal data (with discussion). J. R. Stat. Soc. Ser. B. 42:1980;109-142.
23. Peterson B., Harrell F. E. Partial proportional odds models for ordinal response variables. Appl. Stat. 39:1990;205-217.
24. Scott S. C., Goldberg M. S., Mayo M. E. Statistical assessment of ordinal outcomes in comparative studies. J. Clin. Epidemiol. 50:1997;45-55.
25. Ananth C. V., Kleinbaum D. G. Regression models for ordinal responses: A review of methods and applications. Int. J. Epidemiol. 26:1997;1322-1333.
26. Logistic Regression Examples Using the SAS System. 1995;SAS Institute, Inc. Cary.
27. Steyerberg, E. W, Eijkemans, M. J. C, and, Habbema, J. D. F. Stepwise selection: An underestimated source of overestimation. Submitted.
28. Harrell F. E., Lee K. L., Mark D. B. Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15:1996;361-387.
29. Derksen S., Keselman H. J. Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables. Br. J. Math. Stat. Psychol. 45:1992;265-282.
30. Harrell F. E., Margolis P. A., Gove S., Mason K. E., Mulholland E. K., Lehmann D., Muhe L., Gatchalian S., Eichenwald H. F. Development of a clinical prediction model for an ordinal outcome: The World Health Organization multicentre study of clinical signs and etiological agents of pneumonia, sepsis and meningitis in young infants. Stat. Med. 17:1998;909-944.
31. Hoerl R. W., Schuenemeyer J. H., Hoerl A. E. A simulation of biased estimation and subset regression techniques. Technometrics. 28:1986;369-380.
32. Harrell F. E., Lee K. L., Califf R. M., Pryor D. B., Rosati R. A. Regression modelling strategies for improved prognostic prediction. Stat. Med. 3:1984;143-152.
33. Copas J. B. Regression, prediction and shrinkage (with discussion). J. R. Stat. Soc. Ser. B. 45:1983;311-354.
34. Van Houwelingen J. C., Le Cessie S. Predictive value of statistical models. Stat. Med. 9:1990;1303-1325.
35. Hosmer D. W., Lemeshow S. Applied Logistic Regression. 1989;Wiley, New York.