Discovering Pattern in Medical Audiology Data with FP-Growth Algorithm

G. Noma, Nasir and Mohd Khanapi, Abd Ghani (2012) Discovering Pattern in Medical Audiology Data with FP-Growth Algorithm. In: Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS, 17-19 December, Langkawi, Kedah Malaysia.

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There is potential knowledge inherent in vast amounts of untapped and possibly valuable data generated by healthcare providers. So often, clinicians rely in their skills and experience and that of other medical experts as their source of information. The healthcare sector is now capturing more data in the form of digital and non digital format that may potentially be mined to generate valuable insights. In this paper we propose a five step knowledge discovery model to discover patterns in medical audiology records. We use frequent pattern growth (FP-Growth) algorithm in the data processing step to build the FP-tree data structure and mine it for frequents itemsets. Our aim is to discover interesting itemsets that shows connection between hearing thresholds in pure-tone audiometric data and symptoms from diagnosis and other attributes in the medical records. The experimental results are summaries of frequent structures in the data that contains symptoms of tinnitus, vertigo and giddiness with threshold values and other information like gender.

Item Type: Conference or Workshop Item (Speech)
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Information and Communication Technology > Department of Software Engineeering
Date Deposited: 19 May 2013 10:13
Last Modified: 19 May 2013 10:13
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