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Title: Bayesian belief networks for dementia diagnosis and other applications: a comparison of hand-crafting and construction using a novel data driven technique
Author(s): Oteniya, Lloyd
Supervisor(s): Cowie, Julie
Smith, Leslie S.
Keywords: Bayesian networks
Bayesian network learning
Particle Swarm Optimisation
Dementia diagnosis
Alzheimer's disease
Applications of Bayesian networks
Hand-crafting Bayesian networks
Expert-driven Bayesian networks
Constructing Bayesian networks
Issue Date: Jul-2008
Publisher: University of Stirling
Citation: J. Cowie, L. Oteniya, and R. Coles. Particle swarm optimisation for learn ing bayesian networks. In Sio Iong Ao, Leonid Gelman, David W. L. Hukins, Andrew Hunter, and A. M. Korsunsky, editors, Proceedings of World Congress on Engineering, Lecture Notes in Engineering and Computer Science, pages 71-76. Newswood Limited, 2007.
Lloyd Oteniya, Julie Cowie, and Richard Coles. A clinical decision support system to aid in the diagnosis of dementia. In Proceedings of the 22nd HealthCare Computing Conference, pages 289 - 297, March 2005.
Abstract: 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.
Type: Thesis or Dissertation
Affiliation: School of Natural Sciences
Computing Science and Mathematics

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