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A method for knowledge discovery and development with health data


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Ling, TR (2011) A method for knowledge discovery and development with health data. PhD thesis, University of Tasmania.

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One of the most overlooked problems in the field of knowledge discovery is the
acquisition and incorporation of existing knowledge about the data being analysed
(Fayyad, Piatetsky-Shapiro et al. 1996; Pohle 2003; Kotsifakos, Marketos et al.
2008; Marinica and Guillet 2009). Doing this efficiently and effectively can greatly
improve the relevance and usefulness of the results discovered, particularly for
complex domains with a large amount of existing knowledge (Adejuwon & Mosavi,
2010; C. Zhang, Yu, & Bell, 2009). This study applies the successful Multiple
Classification Ripple Down Rules (MCRDR) knowledge acquisition method to
build a knowledge base from a complex dataset of lung function data, and describes
a method for utilising the dataset to provide additional knowledge validation. The
method acquired knowledge successfully, but indicated that a focus on rule-driven
knowledge acquisition may adversely affect the MCRDR process. Knowledge
acquisition was performed with multiple domain experts, with separate knowledge
bases successfully consolidated using an evidence-based method to quantify
differences and resolve conflicts. This knowledge comparison method was also
tested as a learning and assessment tool for a small group of medical students, with
positive results. In addition, the consolidated expert knowledge base was applied to
the analysis of the lung function data, with a set of common data mining techniques,
to reproduce and expand on a group of published lung function studies. Results
showed that new knowledge could be discovered effectively and efficiently in a
complex domain, despite the user having little domain knowledge themselves.
Results were supported by recent literature, and include findings that may be of
interest in the respiratory field. Notably, newly discovered knowledge is
automatically incorporated into the knowledge base, allowing incremental
knowledge discovery and easy application of those discoveries.

Item Type: Thesis (PhD)
Keywords: knowledge discovery, knowledge acquisition, knowledge comparison
Additional Information:

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Date Deposited: 09 Dec 2011 01:28
Last Modified: 15 Sep 2017 01:06
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