TF-IDF Based Association Rule Analysis System for Medical Data


KIPS Transactions on Software and Data Engineering, Vol. 5, No. 3, pp. 145-154, Mar. 2016
10.3745/KTSDE.2016.5.3.145, Full Text:

Abstract

Because of the recent interest in the u-Health and development of IT technology, a need of utilizing a medical information data has been increased. Among previous studies that utilize various data mining algorithms for processing medical information data, there are studies of association rule analysis. In the studies, an association between the symptoms with specified diseases is the target to discover, however, infrequent terms which can be important information for a disease diagnosis are not considered in most cases. In this paper, we proposed a new association rule mining system considering the importance of each term using TF-IDF weight to consider infrequent but important items. In addition, the proposed system can predict candidate diagnoses from medical text records using term similarity analysis based on medical ontology.


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Cite this article
[IEEE Style]
H. S. Park, M. S. Lee, S. J. Hwang and S. Y. Oh, "TF-IDF Based Association Rule Analysis System for Medical Data," KIPS Transactions on Software and Data Engineering, vol. 5, no. 3, pp. 145-154, 2016. DOI: 10.3745/KTSDE.2016.5.3.145.

[ACM Style]
Ho Sik Park, Min Su Lee, Sung Jin Hwang, and Sang Yoon Oh. 2016. TF-IDF Based Association Rule Analysis System for Medical Data. KIPS Transactions on Software and Data Engineering, 5, 3, (2016), 145-154. DOI: 10.3745/KTSDE.2016.5.3.145.