Discovering Pattern in Medical Audiology Data with FP-Growth Algorithm
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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2012
|
| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/7892/ http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6498100 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|