Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 294)


Bayesian network is a powerful tool to represent and deal with uncertain knowledge. There exists much uncertainty in crop or animal disease. The construction of Bayesian network need much data and knowledge. But when data is scarce, some methods should be adopted to construct an effective Bayesian network. This paper introduces a disease diagnosis model based on Bayesian network, which is two-layered and obeys noisy-or assumption. Based on the two-layered structure, the relationship between nodes is obtained by domain knowledge. Based on the noisy-model, the conditional probability table is elicited by three methods, which are parameter learning, domain expert and the existing certainty factor model. In order to implement this model, a Bayesian network tool is developed. Finally, an example about cow disease diagnosis was implemented, which proved that the model discussed in this paper is an effective tool for some simple disease diagnosis in crop or animal field.


Bayesian Network Domain Expert Disease Diagnosis Disease Node Certainty Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Chard T. Qualitative probability versus quantitative probability in clinical diagnosis: a study using a computer simulation. Med Decis Making. 1991 Jan-Mar;11(1):38–41.CrossRefGoogle Scholar
  2. David J.Spiegelhalter. Bayesian Analysis in Expert Systems, Statistical Science, 1993. Volume 8, Issue 3: 219–247.CrossRefMathSciNetGoogle Scholar
  3. E.Charles, J.Kahn, etc. Construction of a Bayesian network for mammographic diagnosis of breast cancer, Comut. Biol. Med, 1997:19–29.Google Scholar
  4. F.trai..A Bayesian network for predicting yield response of winter wheat to fungicide programs, Computers and electronics in agriculture. 1996: 111–121.Google Scholar
  5. Kevin B.Korb, Ann E.Nicholson. Bayesian Artificial Intelligence, CRC Press.2006:.225–260Google Scholar
  6. Kristian Kristensen etc, The use of a Bayesian network in the design of a decision support system for growing malting barley without use of pesticides, Computers and Electronics in Agriculture, 2002(33):197–217Google Scholar
  7. Nevin Lianwen Zhang. Exploiting causal independence in Bayesian network inference, Journal of artificial intelligence, 1996: 301–328.Google Scholar
  8. P.J.F Lucas. Bayesian network modeling through qualitative patterns. Artificial Intelligence, 2005: 233–263.Google Scholar
  9. P.J.F Lucas. Certainty-Factor-Like structures in Bayesian belief networks, Knowledge-based systems,2001: 327–335.Google Scholar
  10. P.Larranaga, S.Moral. Probabilistic graphical models in artificial intelligence. Applied soft computing. 2008:1–18.Google Scholar
  11. Radim Jirousck. Constructing probabilistic models, International journal of medical informatics 1997(45): 9–18.Google Scholar
  12. Wang ronggui etc, From Certainty Factor Model to Bayesian Network. computer science, 2004, 31(10).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Jilin UniversityJilinChina
  2. 2.Jilin Agricultural UniversityJilinChina

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