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Proc Annu Symp Comput Appl Med Care. 1993 : 485–491.
PMCID: PMC2850625

An evaluation of factors influencing Bayesian learning systems.


This paper examines the influences of situational and model factors upon the accuracy of Bayesian learning systems. In particular, it is concerned with the impact of variations in training sample size, number of attributes, choice of Bayesian model, and criteria for excluding model attributes upon the overall accuracy of the simple and proper Bayes models.

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Selected References

These references are in PubMed. This may not be the complete list of references from this article.
  • Gustafson DH, Kestly JJ, Greist JH, Jensen NM. Initial evaluation of a subjective Bayesian diagnostic system. Health Serv Res. 1971 Fall;6(3):204–213. [PMC free article] [PubMed]
  • Norusis MJ, Jacquez JA. Diagnosis. I. Symptom nonindependence in mathematical models for diagnosis. Comput Biomed Res. 1975 Apr;8(2):156–172. [PubMed]
  • Fryback DG. Bayes' theorem and conditional nonindependence of data in medical diagnosis. Comput Biomed Res. 1978 Oct 5;11(5):423–434. [PubMed]
  • Chard T. Self-learning for a Bayesian knowledge base: how long does it take for the machine to educate itself? Methods Inf Med. 1987 Oct;26(4):185–188. [PubMed]
  • Gammerman A, Thatcher AR. Bayesian diagnostic probabilities without assuming independence of symptoms. Methods Inf Med. 1991;30(1):15–22. [PubMed]
  • Bigongiari LR, Preston DF, Cook L, Dwyer SJ, 3rd, Fritz S, Fryback DG, Thornbury JR. Uncertainty/information as measure of various urographic parameters: an information theory model of diagnosis of renal masses. Invest Radiol. 1981 Jan-Feb;16(1):77–81. [PubMed]
  • Alemi F, Rice J, Hankins R. Predicting in-hospital survival of myocardial infarction. A comparative study of various severity measures. Med Care. 1990 Sep;28(9):762–775. [PubMed]
  • Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985 Oct;13(10):818–829. [PubMed]
  • Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982 Apr;143(1):29–36. [PubMed]
  • McNeil BJ, Hanley JA. Statistical approaches to the analysis of receiver operating characteristic (ROC) curves. Med Decis Making. 1984;4(2):137–150. [PubMed]

Articles from Proceedings of the Annual Symposium on Computer Application in Medical Care are provided here courtesy of American Medical Informatics Association