Bayesian Network Inference with Qualitative Expert Knowledge for Decision Support Systems

Verbal decision analysis (VDA) is a relatively new term introduced in Larichev and Moshkovich (1997) for a methodological approach to discrete multi-criteria decision making (MCDM) problems that was under elaboration by Russian researchers since the 1970s. Its main ideas, principles, and strength in comparison with other approaches to MCDM problems are summarized in Moshkovich, Mechitov, and Olson (2005) and in posthumous book (Larichev, 2006) as follows: problem description (alternatives, criteria, and alternatives’ estimates upon criteria) with natural language without any conversion to numerical form; usage of only those operations of eliciting information from a decision maker (DM) that deems to be psychologically reliable; control of DM’s judgments consistency, and traceability of results, that is, the intermediate and final results of a problem solution have to be explainable to DM. The main objective of this chapter is to provide an analysis of the methods and models of VDA for implementing them in intellectual decision support systems. We start with an overview of existing approaches to VDA methods and model representation. In the next three sections we present examples of implementing the methods and models of VDA for intellectual decision support systems designed for such problems solving as discrete multi-criteria choice, construction of expert knowledge base, and multi-criteria assignment problem. Finally, we analyze some perspective of VDA-based methods to implement them for intellectual decision support systems.

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Extraction of Belief Knowledge from a Relational Database for Quantitative Bayesian Network Inference

Mathematical Problems in Engineering ◽

10.1155/2013/297121 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

LiMin Wang

Keyword(s):

Bayesian Network ◽

Relational Database ◽

Network Inference ◽

Probabilistic Reasoning ◽

Expert Knowledge ◽

Learning Framework ◽

Three Phase ◽

Rule Analysis ◽

Effectiveness And Efficiency ◽

Bayesian Network Inference

The problem of extracting knowledge from a relational database for probabilistic reasoning is still unsolved. On the basis of a three-phase learning framework, we propose the integration of a Bayesian network (BN) with the functional dependency (FD) discovery technique. Association rule analysis is employed to discover FDs and expert knowledge encoded within a BN; that is, key relationships between attributes are emphasized. Moreover, the BN can be updated by using an expert-driven annotation process wherein redundant nodes and edges are removed. Experimental results show the effectiveness and efficiency of the proposed approach.

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Decision Support System for Hepatitis Disease Diagnosis using Bayesian Network

Sukkur IBA Journal of Computing and Mathematical Sciences ◽

10.30537/sjcms.v1i2.51 ◽

2017 ◽

Vol 1 (2) ◽

pp. 11

Author(s):

Shamshad Lakho ◽

Akhtar Hussain Jalbani ◽

Muhammad Saleem Vighio ◽

Imran Ali Memon ◽

Saima Siraj Soomro ◽

...

Keyword(s):

Decision Support ◽

Bayesian Network ◽

Decision Support Systems ◽

Medical Diagnosis ◽

Support Systems ◽

Signs And Symptoms ◽

Disease Diagnosis ◽

Treatment Recommendation ◽

Knowledge Model ◽

Computerized Decision Support

Medical judgments are tough and challenging as the decisions are often based on the deficient and ambiguous information. Moreover, the result of decision process has direct effects on human lives. The act of human decision declines in emergency situations due to the complication, time limit, and high risks. Therefore, provision of medical diagnosis plays a dynamic role, specifically in the preliminary stage when a physician has limited diagnosis experience and identifies the directions to be taken for the treatment process. Computerized Decision Support Systems have brought a revolution in the medical diagnosis. These automatic systems support the diagnosticians in the course of diagnosis. The major role of Decision Support Systems is to support the medical personnel in decision-making procedures regarding disease diagnosis and treatment recommendation. The proposed system provides easy support in Hepatitis disease recognition. The system is developed using the Bayesian network model. The physician provides the input to the system in the form of symptoms stated by the patient. These signs and symptoms match with the casual relationships present in the knowledge model. The Bayesian network infers conclusion from the knowledge model and calculates the probability of occurrence of Hepatitis B, C and D disorders.

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