In contrast, a Bayesian network model derived using artificial
intelligence is capable of expressing the dependency and correlation
between variables from a quantitative and qualitative
perspective and taking into consideration the uncertain nature of
the features. Furthermore, a Bayesian network can update the set
of conditional probability tables as new training samples are
added, potentially leading to a higher detection accuracy and thus
a lower respondent burden