Skip to main content
SpringerLink
Log in

The Discovery of Prognosis Factors Using Association Rule Mining in Acute Myocardial Infarction with ST-Segment Elevation

  • Conference paper
  • First Online:
Information Technology in Bio- and Medical Informatics (ITBAM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9267))

Abstract

Association rule mining has been applied actively in order to discover the hidden factors in acute myocardial infarction. There has been minimal research regarding the prognosis factor of acute myocardial infarction, and several previous studies has some limitations which are generation of incorrect population and potential data bias. Thus, we suggest the generation of prognosis factor based on association rule mining for acute myocardial infarction with ST-segment elevation. In our experiments, we obtain high interestingness factor based on Korean acute myocardial infarction registry which is corrected by 51 participating hospitals since 2005. The interestingness of the factor is evaluated by confidence. It is expected to contribute to prognosis management by high reliability factor.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 34.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 44.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. The Textbook of Cardiovascular medicine: The Korea Society Of Circulation (2004)

    Google Scholar 

  2. Karaolis, M., Moutris, J.A., Papaconstantinou, L., Pattichis, C.S.: Association rule analysis for the assessment of the risk of coronary heart events. In: 31th Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA (2009)

    Google Scholar 

  3. Lee, D.G., Ryu, K.S., Bashir, M., Bae, J.W., Ryu, K.H.: Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction. J. Med. Syst. 37(2), 9896 (2013)

    Article  Google Scholar 

  4. Antman, E.M., Hand, M., Armstrong, P.W., Bates, E.R.: 2007 Focused up date of the ACC/AHA 2004 guidelines for the management of patients with ST-elevation myocardial infarction. In: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines, Circulation, pp. 296–329 (2007)

    Google Scholar 

  5. Anderson, J.L., Antman, E.M., Adams, C.D., Bridges, C.R.: ACC/AHA 2007 guidelines for the management of patients with unstable angina/non ST-elevation myocardial infarction: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines, Circulation, pp. 148–304 (2007)

    Google Scholar 

  6. Johns Hopkins Medicine. www.hopkinsmedicine.org/healthlibrary

  7. Kalla, K., Christ, G., Karnik, R., Malzer, R., Norman, G., Prachar, H., Schreiber, W., Unger, G., Glogar, H.D., Kaff, A., Laggner, A.N., Maurer, G., Mlczoch, J., Slany, J., Weber H.S., Huber, K.: Implementation of Guidelines Improves the Standard of Care: The Viennese Registry on Reperfusion Strategies in ST-Elevation Myocardial Infarction (Vienna STEMI Registry), Circulation, vol. 113, pp. 2398–2405 (2006)

    Google Scholar 

  8. De Luca, G., Suryapranata, H., Ziklstra, F., van’t Hof, A.W., Hoorntje, J.C., Gosselink, A.T., Dambrink, J.H.: Symtom-onset-to-balloon time and mortality in patients with acute myocardial infarction treated by primary angioplasty. J. Am. Coll. Cardiol. 42, 991–997 (2003)

    Article  Google Scholar 

  9. Tan, PN., Michael, S., Vioin, K.: Introduction to Data Mining (2006)

    Google Scholar 

  10. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Moragan Kaufmann Publisher, San Francisco (2005)

    MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No.2013R1A2A2A01068923) and by the ITRC(Information Technology Research Center) support program (NIPA-2014-H0301-14-1002).

Author information

Authors and Affiliations

  1. Database / Bioinformatics Laboratory, Chungbuk National University, Cheongju-si, South Korea

    Kwang Sun Ryu, Hyun Woo Park, Soo Ho Park, Ibrahim M. Ishag & Keun Ho Ryu

  2. Division of Cardiology, Department of Internal Medicine, Chungbuk National University, Cheongju city, Chungbuk, Korea

    Jang Hwang Bae

Authors
  1. Kwang Sun Ryu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyun Woo Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Soo Ho Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ibrahim M. Ishag
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jang Hwang Bae
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Keun Ho Ryu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Keun Ho Ryu .

Editor information

Editors and Affiliations

  1. Institute of Informatics and Telematics, Pisa, Italy

    M. Elena Renda

  2. Czech Technical University in Prague, Prague, Czech Republic

    Miroslav Bursa

  3. Medical University Graz, Graz, Austria

    Andreas Holzinger

  4. San Jose State University, San Jose, California, USA

    Sami Khuri

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Ryu, K.S., Park, H.W., Park, S.H., Ishag, I.M., Bae, J.H., Ryu, K.H. (2015). The Discovery of Prognosis Factors Using Association Rule Mining in Acute Myocardial Infarction with ST-Segment Elevation. In: Renda, M., Bursa, M., Holzinger, A., Khuri, S. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2015. Lecture Notes in Computer Science(), vol 9267. Springer, Cham. https://doi.org/10.1007/978-3-319-22741-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22741-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22740-5

  • Online ISBN: 978-3-319-22741-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation