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unlike Bayesian networks and ANN, does not have a tendency to generalize too much,
which results in superior accuracy when proposing a solution derived from the memorized
cases. Association rules mining is a technique used for extraction of significant correlations
of frequent patterns, clusters, or causal structures among database items.
Zhuang (Zhuang et al., 2009) describes a new methodology of integrating data mining and
CBR for intelligent decision support for pathology ordering. The purpose of the integration
of data mining and CBR is gathering of knowledge from historical data using data mining,
and retrieve and use these data for decision making support.
As the knowledge base for rule based reasoning, case library is a fundamental building
block for decision support systems that use case based reasoning. For similar reasons as for
rule based reasoning, CBR technique is not eligible for application in laboratory research.
3.3 Bayesian believe networks (BBN)
Bayesian believe network is a directed acyclic graph G = ( V , E , P ), where V is a set of nodes
representing random variables, E is a set of edges representing the relationships and
dependencies between these variables, and P represents associated probability distributions
on those variables. It is a graphics model capable of representing the relationships between
variables in a problem domain.
In other words, BBN is directed graphical model where an edge from A to B can be
informally interpreted as indicating that A "causes" B. The example of a simple BBN is in
Figure 2. Nodes represent binary random variables. The event "grass is wet" (W=true) has
two possible causes: either the water sprinkler is on (S=true) or it is raining (R=true). The
strength of this relationship is shown in the table below W; this is called W's conditional
probability distribution (NNMI Lab., 2007).
Fig. 2. A simple Bayesian network (NNMI Lab., 2007)
Bayesian networks represent good combination of the data and prior expert knowledge.
Relationships between the variables in a problem domain can be interpreted both causally
and probabilistically. BBN is able to cope with the situations where some data are missing,
and unlike the rule based systems allow the capture of a broader context.
The application of Bayesian probability theory in diagnostic tasks addresses Lindgaard
(Lindgaard et al., 2007). He claims that the Bayesian algorithm is a suitable alternative for
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