1 research outputs found
Bayesian belief networks for dementia diagnosis and other applications: a comparison of hand-crafting and construction using a novel data driven technique
The Bayesian network (BN) formalism is a powerful representation for
encoding domains characterised by uncertainty. However, before it
can be used it must first be constructed, which is a major challenge
for any real-life problem. There are two broad approaches, namely
the hand-crafted approach, which relies on a human expert, and the
data-driven approach, which relies on data. The former approach is
useful, however issues such as human bias can introduce errors into
the model. We have conducted a literature review of the
expert-driven approach, and we have cherry-picked a number of common
methods, and engineered a framework to assist non-BN experts with
expert-driven construction of BNs. The latter construction approach
uses algorithms to construct the model from a data set. However,
construction from data is provably NP-hard.
To solve this problem, approximate, heuristic algorithms have been
proposed; in particular, algorithms that assume an order between the
nodes, therefore reducing the search space. However, traditionally,
this approach relies on an expert providing the order among the
variables
--- an expert may not always be available, or may be unable to
provide the order. Nevertheless, if a good order is available, these
order-based algorithms have demonstrated good performance. More
recent approaches attempt to ``learn'' a good order then use the
order-based algorithm to discover the structure. To eliminate the
need for order information during construction, we propose a search
in the entire space of Bayesian network structures --- we present a
novel approach for carrying out this task, and we demonstrate its
performance against existing algorithms that search in the entire
space and the space of orders.
Finally, we employ the hand-crafting framework to construct models
for the task of diagnosis in a ``real-life'' medical domain,
dementia diagnosis. We collect real dementia data from clinical
practice, and we apply the data-driven algorithms developed to
assess the concordance between the reference models developed by
hand and the models derived from real clinical data