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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2013 Vol.3(3): 294-296 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.324

Mining Medical Databases Using Graph based Association Rules

Wael Ahmad AlZoubi
Abstract—Medical databases have accumulated huge amounts of information about patients and their medical conditions. Relationships and patterns within these data can provide new medical knowledge. Sorry to say that few methodologies have been developed and applied to discover this hidden knowledge. In this paper, the graph bases association rules mining (data mining is the main part of Knowledge Discovery in Databases) is used to search for relationships in a large medical database. The data that was collected on 6549 obstetrical patients were evaluated for factors potentially contributing to preterm birth using exploratory factor analysis. This paper describes the processes involved in mining a medical database including data warehousing, data query and cleaning, and data analysis.

Index Terms—Graph, medical database, rule mining.

W. A. AlZoubi is with the University Kebangsaan Malaysia (e-mail: wz@ftsm.ukm.my).


Cite:Wael Ahmad AlZoubi, "Mining Medical Databases Using Graph based Association Rules," International Journal of Machine Learning and Computing vol.3, no. 3, pp. 294-296, 2013.

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