A review and comparison of classification algorithms for medical decision making

Health Policy

Volumn 71, Issue 3, 2005, Pages 315-331

  Harper, Paul R ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

CART; Classification algorithms; Clinical decision making; Neural networks

Indexed keywords

ACCURACY; ALGORITHM; ARTIFICIAL NEURAL NETWORK; CALCULATION; CLASSIFICATION; DATA BASE; DIAGNOSTIC VALUE; DISCRIMINANT ANALYSIS; HEALTH CARE NEED; HEALTH CARE UTILIZATION; HUMAN; MEDICAL DECISION MAKING; REGRESSION ANALYSIS; REVIEW; STATISTICAL SIGNIFICANCE; TREATMENT PLANNING;

EID: 13444291932     PISSN: 01688510     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.healthpol.2004.05.002     Document Type: Article

References (19)

1. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. London: Chapman & Hall; 1984.
2. Remme J., Habbema J.D.F., Hermans J. A simulative comparison of linear, quadratic and kernel discrimination. Journal of Statistics and Computer Simulation. 11:1980;87-106
3. Clark P, Boswell R. Rule induction with CN2: some recent improvements. In: Proceedings of ESWL'91, Porto, Portugal; 1991. p. 151-163.
4. Xu L., Krzyzak A., Oja E. Neural nets for dual subspace pattern recognition method. International Journal of Neural Systems. 2:1991;169-184
5. King R.D., Feng C., Sutherland A. Statlog: comparison of classification algorithms on large real-world problems. Applied Artificial Intelligence. 9:1995;289-333
6. Draper NR, Smith H. Applied regression analysis. New York: Wiley; 1966.
7. Smith M.E., et al. Case-mix groups for hospital-based home care. Medical Care. 30:1992;1-16
8. Hornberger J.C., Habraken H., Bloch D.A. Minimum data needed on patient preferences for acute, efficient medical decision-making. Medical Care. 33:1995;297-310
9. Garbe C., et al. Primary Cutaneous Melanoma - identification of prognostic groups and estimation of individual prognosis for 5,093 patients. Cancer. 75:1995;2484-2491
10. Ridley S. Classification trees: a possible method for iso-resource grouping in intensive care. Anaesthesia. 53:1998;833-840
11. Harper P.R., Shahani A.K. Modelling for the planning and management of bed capacities in hospitals. Journal of the Operational Research Society. 53:2002;11-19
12. Berzal F., et al. On the quest for easy-to-understand splitting rules. Data and Knowledge Engineering. 44:2003;31-48
13. Anderson JA. An introduction to neural networks. Cambridge, MA: MIT Press; 1995.
14. Haykin S. Neural networks: a comprehensive foundation. London: Prentice-Hall; 1999.
15. Callan R. The essence of neural networks. London: Prentice-Hall; 1999.
16. Kay JW, Titterington DM. Statistics and neural networks: advances at the interface. Oxford: Oxford University Press; 1999.
17. Mehrotha K, et al. Element of artificial neural networks. Cambridge, MA: MIT Press; 1997.
18. Fahlman S. Distributed connectionist systems for AI: prospects and problems. In: Concepts and characteristics of knowledge-based systems, proceedings of IFIP 10/WG 10.1 workshop. Mount Fuji, Japan; 1999. p. 22-35.
19. Manly BFJ. Multivariate Statistical Methods. London: Chapman & Hall; 1998.