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 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.