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the diagnosis of disease, and thus to provide effective decision support in medical
diagnostic systems. The result of Bayesian analysis is a set of hypotheses associated with the
probability distribution. To develop decision rules, these probabilities are combined with
information about the nature of possible decisions, their significance and relevance.
Probabilities of Bayesian network nodes are then reviewed and further refine in each of the
next iteration with new information set.
Liu and Li (Liu & Li, 2007) bring the example of using diagnostic DSS outside the field of
clinical medicine. They used Bayesian networks to build a DSS for machinery maintenance,
using BNN's suitability for applications in fault diagnostics. Described DSS supports the
strategy of proactive maintenance based on monitoring and diagnosis of machines, and
forecasting and prevention of disorders.
Using of Bayesian believe networks for the decision support poses two major problems. The
first is the need of mastering the Bayesian probability theory, which means managing
relatively large and robust mathematical apparatus. The second is the considerable
computation complexity of algorithms for learning BBN from data, respectively difficult
inference in large models.
Although Bayesian networks seem to be an appropriate technology for decision support in
the field of laboratory research, above mentioned difficulties make the development of DSS
applications only through own forces very difficult or even impossible for many users.
3.4 Artificial neural networks (ANN)
Artificial neural network is a computational model derived from the way of information
processing performed by human brain. ANN consists of simple interconnected elements for
data processing - artificial neurons. These elements process the data in parallel and
collectively, in a similar way as the biological neurons. Artificial neural networks have some
desirable properties similar to biological neural networks, such as learning ability, self-
organization, and fault tolerance (Turban et al., 2008).
The basic element of the artificial neural network is an artificial neuron. Artificial neuron is a
computation unit with inputs, outputs, internal states and parameters. This unit processes
the input data (signals) and generates appropriate outputs. There are several types of
artificial neurons, which vary according to the type of neural network. Generic type of
artificial neuron, so called formal neuron, is shown in Figure 3.
Fig. 3. Formal neuron
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