Comprehensive Association Rules Mining of Health Examination Data with an Extended FP-Growth Method

Bowei Wang, Dan Chen*, Benyun Shi, Jindong Zhang, Yifu Duan, Jingying Chen, Ruimin Hu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

24 Citations (Scopus)


With the booming of social media and health informatics, there exists a pressing need for a powerful tool to sustain comprehensive analysis of public and personal health information. In particular, it should be able (1) to maximize the discovery of association rules amongst data items and (2) to handle the rapid growing data scale. The FP-Growth algorithm is a salient association rule learning method in exploring potential relation in database possibly with a lack of priori knowledge. It has the merits of low time & space complexity, whereas it cannot handle negative association rules which is necessary in comprehensive mining of health data. In order to enable comprehensive discovery of association rules, this study extends the FP-Growth algorithm to mine both positive and negative frequent patterns, namely the PNFP-Growth framework. The extended approach also adopts a prune strategy to filter out misleading patterns to the most by correlating the negative data items and the positive ones. Experiments had been performed to evaluate the performance of the PNFP-Growth over a public data set and a database consisting of thousands of people’s real health examination information (collected within 5 years from the date of this publication). The results indicate that (1) the PNFP-Growth can excavate more patterns than the traditional counterpart does while it is still highly efficient, and (2) the analysis upon the health examination data is informative and well complies with the clinical practices, e.g., more than 30 % people suffering from hypertension are having high systolic pressure and liver problems.

Original languageEnglish
Pages (from-to)267-274
Number of pages8
JournalMobile Networks and Applications
Issue number2
Publication statusPublished - 1 Apr 2017

Scopus Subject Areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications

User-Defined Keywords

  • Association rules
  • Data mining
  • FP-growth
  • Health examination data
  • Health informatics
  • Negative association rules


Dive into the research topics of 'Comprehensive Association Rules Mining of Health Examination Data with an Extended FP-Growth Method'. Together they form a unique fingerprint.

Cite this