Abstract: The work is devoted to methods and software tools of designing intelligent decision support systems (IDSS), which helps professionals (decision making persons) helping to diagnose complex problem situations on the example of complicated pathologies of view. Unlike traditional Bayesian belief networks, the proposed application of advanced multilevel (difficult-structured) networks, more convenient for complex research of the problem and providing expert data. Integration of Bayesian belief networks and Dempster-Shafer? method allows using at diagnostics both expert data, and numerical (probabilistic) data obtained in the result of measurements. The proposed approach is implemented in the prototype of the intelligent decision support system for diagnostics of difficult diseases of vision.

Keywords: intelligent system, decision support, diagnostics, problem situation, Bayesian belief network, Demster-Shafer? method.

ACM Classification Keywords: H.4.2 Information systems applications: Types of systems – Decision support; I.2.3 Artificial intelligence: Deduction and Theorem Proving – Uncertainty, "fuzzy," and probabilistic reasoning; I.2.4 Artificial intelligence: Knowledge Representation Formalisms and Methods – Bayesian belief network.

Link:

THE INTELLIGENT DECISION SUPPORT SYSTEM FOR DIAGNOSTIC OF DIFFICULT DISEASES OF VISION

Aleksandr Eremeev, Ruslan Khaziev, Irina Tcapenko, Marina Zueva

http://www.foibg.com/ijicp/vol01/ijicp01-03-p06.pdf