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Applications of association rule mining in health informatics: a survey

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Abstract

Association rule mining is an effective data mining technique which has been used widely in health informatics research right from its introduction. Since health informatics has received a lot of attention from researchers in last decade, and it has developed various sub-domains, so it is interesting as well as essential to review state of the art health informatics research. As knowledge discovery researchers and practitioners have applied an array of data mining techniques for knowledge extraction from health data, so the application of association rule mining techniques to health informatics domain has been focused and studied in detail in this survey. Through critical analysis of applications of association rule mining literature for health informatics from 2005 to 2014, it has been explored that, instead of the more efficient alternative approaches, the Apriori algorithm is still a widely used frequent itemset generation technique for application of association rule mining for health informatics. Moreover, other limitations related to applications of association rule mining for health informatics have also been identified and recommendations have been made to mitigate those limitations. Furthermore, the algorithms and tools utilized for application of association rule mining have also been identified, conclusions have been drawn from the literature surveyed, and future research directions have been presented.

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Notes

  1. EuroMISE Project STULONG, http://euromise.vse.cz/stulong-en/index.php. Accessed \(5{\mathrm{th}}\) December 2014.

  2. Cleveland Heart Disease Dataset, https://archive.ics.uci.edu/ml/datasets/Heart+Disease. Accessed \(5{\mathrm{th}}\) December 2014.

  3. TREC 2011 Medical Track Dataset, http://trec.nist.gov/data/medical2011.html. Accessed 5th December 2014.

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Authors and Affiliations

  1. Department of Computer Science, Universität des Saarlandes, Saarbruecken, Germany

    Wasif Altaf

  2. Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan

    Muhammad Shahbaz

  3. Ted Rogers School of Management, Ryerson University, Toronto, ON, Canada

    Aziz Guergachi

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  1. Wasif Altaf
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  2. Muhammad Shahbaz
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  3. Aziz Guergachi
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Correspondence to Wasif Altaf.

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Altaf, W., Shahbaz, M. & Guergachi, A. Applications of association rule mining in health informatics: a survey. Artif Intell Rev 47, 313–340 (2017). https://doi.org/10.1007/s10462-016-9483-9

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