Skip to main content
SpringerLink
Log in
Book cover

EPIA Conference on Artificial Intelligence

EPIA 2017: Progress in Artificial Intelligence pp 204–214Cite as

Discovering Interesting Associations in Gestation Course Data

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10423))

Abstract

Finding risk factors in pregnancy related to neonatal hypoxia is a challenging task due to the informal nature and a wide scatter of the data. In this work, we propose a methodology for sequential estimation of interestingness of association rules with two sets of criteria. The rules suggest that a strong relationship exists between the specific sets of attributes and the diagnosis. We set up a profile of the pregnant woman with a high likelihood of hypoxia of the newborn that would be beneficial to medical professionals.

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

References

  1. Seikku, L., Rahkonen, L., Tikkanen, M., Hämäläinen, E., Rahkonen, P., Andersson, S., Teramo, K., Paavonen, J., Stefanovic, V.: Amniotic fluid erythropoietin and neonatal outcome in pregnancies complicated by intrauterine growth restriction before 34 gestational weeks. Acta Obstet. Gynecol. Scand. 94(3), 288–294 (2015)

    Article  Google Scholar 

  2. Martinez-Biarge, M., Diez-Sebastian, J., Wusthoff, C.J., Mercuri, E., Cowan, F.M.: Antepartum and intrapartum factors preceding neonatal hypoxic-ischemic encephalopathy. Pediatrics 132(4), e952–e959 (2013)

    Article  Google Scholar 

  3. Mertsalova, O.V.: Perinatal hypoxic injuries of fetal CNS in high-risk pregnant women (diagnosis, prognosis of outcomes, optimization of pregnancy and delivery management) (2002)

    Google Scholar 

  4. Antonucci, R., Porcella, A., Pilloni, M.D.: Perinatal asphyxia in the term newborn. J. Pediatr. Neonatal Individ. Med. 3(2), e030269 (2014)

    Google Scholar 

  5. Hypoxic-Ischaemic Encephalopathy (HIE). Queensland Clinical Guidelines (2016)

    Google Scholar 

  6. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Conference, pp. 207–216 (1993)

    Article  Google Scholar 

  7. Kumar, K.P., Arumugaperumal, S.: Association rule mining and medical application: a detailed survey. Int. J. Comput. Appl. 80(17), 0975–8887 (2013)

    Google Scholar 

  8. Chaves, R., Gorriz, J.M., Ramirez, J., Illan, I.A., Salas-Gonzalez, D., Gomez-Rio, M.: Efficient mining of association rules for the early diagnosis of Alzheimer’s disease. Phys. Med. Biol. 56, 6047–6063 (2011)

    Article  Google Scholar 

  9. Cheng, C.-W., Chanani, N., Enugopalan, J., Maher, K., Wang, M.D.: icuARM - an ICU clinical decision support system using association rule mining. IEEE J. Transl. Eng. Health Med. 1, 4400110 (2013)

    Article  Google Scholar 

  10. Ordonez, C., Santana, C.A., de Braal, L.: Discovering interesting association rules in medical data. In: ACM DMKD Workshop, pp. 78–85 (2000)

    Google Scholar 

  11. Kaur, H., Wasan, S.K., Al-Hegami, A.S., Bhatnagar, V.: A unified approach for discovery of interesting association rules in medical databases. In: Perner, P. (ed.) ICDM 2006. LNCS, vol. 4065, pp. 53–63. Springer, Heidelberg (2006). doi:10.1007/11790853_5

    Chapter  Google Scholar 

  12. Reps, J.M., Aickelin, U., Ma, J., Zhang, Y.: Refining adverse drug reactions using association rule mining for electronic healthcare data. In: IEEE International Conference on Data Mining Workshop, pp. 763–770 (2014)

    Google Scholar 

  13. Simovici, D.A.: Data Mining of Medical Data: Opportunities and Challenges in Mining Association Rules (2012). www.cs.umb.edu/~dsim/papersps/dmmd.pdf

  14. Skarga-Bandurova, I., Biloborodova, T.: Exploratory data analysis to identifying meaningful factors of hypoxic fetal injuries. Inf. Model. 44(1216), 122–135 (2016). Herald of the NTU “KhPI”. NTU “KhPI”, Kharkov. doi:10.20998/2411-0558.2016.44.09

  15. Hahsler, M., Grun, B., Hornik, K., Buchta, C.: Introduction to arules – a computational environment for mining association rules and frequent item sets (2005). http://www.lsi.upc.edu/~belanche/Docencia/mineria/Practiques/R/arules.pdf

  16. Hegland, M.: The apriori algorithm – a tutorial. WSPC/Lect. Notes Ser. 9(7) (2005). http://www2.ims.nus.edu.sg/preprints/2005-29.pdf

  17. Doddi, S., Marathe, A., Ravi, S.S., Torney, D.C.: Discovery of Association Rules in Medical Data. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.198.4047&rep=rep1&type=pdf

  18. Zhao, Y., Zhang, C., Cao, L.: Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction. Information Science Reference (2009)

    Google Scholar 

  19. Masood, A., Ouaguenouni, S.: Probabilistic measures for interestingness of deviations – a survey. Int. J. Artif. Intell. Appl. 4, 1 (2013)

    Google Scholar 

  20. Billing, V.A.: Association rule mining for medical diagnostics. Softw. Solut. Syst. 2, 146–157 (2016)

    Google Scholar 

  21. Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inf. 3(2), 119–131 (2016)

    Article  Google Scholar 

  22. Yildirim, P., Ekmekci, I.O., Holzinger, A.: On knowledge discovery in open medical data on the example of the FDA drug adverse event reporting system for alendronate (fosamax). In: Holzinger, A., Pasi, G. (eds.) HCI-KDD 2013. LNCS, vol. 7947, pp. 195–206. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39146-0_18

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Volodymyr Dahl East Ukrainian National University, Severodonetsk, Ukraine

    Inna Skarga-Bandurova, Tetiana Biloborodova & Maksym Nesterov

Authors
  1. Inna Skarga-Bandurova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tetiana Biloborodova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maksym Nesterov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Inna Skarga-Bandurova .

Editor information

Editors and Affiliations

  1. Universidade do Porto, Porto, Portugal

    Eugénio Oliveira

  2. Universidade do Porto, Porto, Portugal

    João Gama

  3. Polytechnic Institute of Porto, Porto, Portugal

    Zita Vale

  4. Universidade do Porto, Porto, Portugal

    Henrique Lopes Cardoso

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Skarga-Bandurova, I., Biloborodova, T., Nesterov, M. (2017). Discovering Interesting Associations in Gestation Course Data. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-65340-2_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65339-6

  • Online ISBN: 978-3-319-65340-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation