Bayesian network construction using a fuzzy rule based approach for medical decision support

Vasilios Zarikas, Elpiniki Papageorgiou, Peter Regner

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


This study proposes a novel method for the construction of efficient and convenient Bayesian networks (BNs) and influence diagrams regarding medical problems based on fuzzy rules. The general methodology that was developed is able to address decisions based on fuzzy medical rules that connect symptoms with the severity/rating scale of a disease. These fuzzy rules are rich enough to cover a large variety of medical decisions. The method overcomes the major disadvantage of Bayesian nets, that is, the need of a vast amount of subjective probabilities. Instead of filling conditional probability tables in BNs, physicians report their knowledge in the form of fuzzy rules. The knowledge of these rules after defuzzification is transformed according to certain equations into probabilities. This becomes possible, categorizing the rules into certain types. For each type of rule, a mathematical expression is determined, which sets correctly the conditional probabilities that relate a symptom with its child severity. A particular example of assessing pulmonary infections and making decisions on severity degree was explored, implementing a decision support system (DSS) to show the functionality of the proposed methodology. The developed front-end DSS was evaluated and proved capable to drive fair decisions close to those obtained by physicians.

Original languageEnglish
Pages (from-to)344-369
Number of pages26
JournalExpert Systems
Issue number3
Publication statusPublished - Jun 1 2015


  • Bayesian networks
  • fuzzy logic
  • fuzzy rules
  • medical decision-making

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

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