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Journal of Heuristics

, Volume 25, Issue 4–5, pp 703–729 | Cite as

Patient classification considering the risk of restenosis after coronary stent placement

  • Halenur Şahin
  • Serhan Duran
  • Ertan YakıcıEmail author
  • Mahmut Şahin
Article
  • 127 Downloads

Abstract

Aging and some lifestyle habits cause plaque accumulation in the blood vessels of the heart and this causes narrowing of the arteries. Stents are tiny wire mesh tubes which are used in balloon angioplasty to keep the vessels open. However, the stented vessel has a risk of re-narrowing due to the recovery response of the stented vessel segment and this is called in-stent-restenosis. The objective of this study is classifying patients according to their risks of restenosis. For this purpose, first a utilites additives discriminates model called parametrized classification model is developed, then to improve the classification performance of this model, a non-dominated sorting based multi-objective evolutionary algorithm (NSGA-II) is implemented. Finally, computational experiments are conducted with real life data to demonstrate the efficiency of proposed methods.

Keywords

Healthcare Classification Evolutionary algorithms 

Notes

Acknowledgements

The research of the first author is supported by the Scientific and Technological Research Council of Turkey, Grant Nos. BIDEB-2211-A and BIDEB-2214-A.

References

  1. Cassese, S., Byrne, R.A., Tada, T., Pinieck, S., Joner, M., Ibrahim, T., King, L.A., Fusaro, M., Laugwitz, K.L., Kastrati, A.: Incidence and predictors of restenosis after coronary stenting in 10 004 patients with surveillance angiography. Heart 100(2), 153–159 (2014)Google Scholar
  2. Coello, C.A.C., Lamont, G.B., Van Veldhuizen, D.A., et al.: Evolutionary Algorithms for Solving Multi-objective Problems, vol. 5. Springer, Berlin (2007)zbMATHGoogle Scholar
  3. Dangas, G., Kuepper, F.: Restenosis: repeat narrowing of a coronary artery prevention and treatment. Circulation 105(22), 2586–2587 (2002)CrossRefGoogle Scholar
  4. Das, S., Panigrahi, B.K. (2009). Multi-objective evolutionary algorithms. In: Rabuñal Dopico, J., Dorado, J., Pazos, A. (eds.) Encyclopedia of Artificial Intelligence, pp 1145–1151. IGI Global, Hershey, PA (2009).  https://doi.org/10.4018/978-1-59904-849-9.ch167
  5. Deb, K.: Multi-objective optimization using evolutionary algorithms, vol. 16. Wiley, New York (2001)zbMATHGoogle Scholar
  6. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  7. Doğan, A., Kozan, Ö., Tüzün, N.: Stent-içi restenozun fizyopatolojisi ve tedavisi. Türk Kardiyol Dern Arş 33, 115–25 (2005)Google Scholar
  8. Doumpos, M., Zopounidis, C.: Assessing financial risks using a multicriteria sorting procedure: the case of country risk assessment. Omega 29(1), 97–109 (2001)CrossRefGoogle Scholar
  9. Doumpos, M., Zopounidis, C.: On the use of a multi-criteria hierarchical discrimination approach for country risk assessment. J. Multi-Criteria Decis. Anal. 11(4–5), 279–289 (2002)CrossRefzbMATHGoogle Scholar
  10. Greco, S., Figueira, J., Ehrgott, M.: Multiple Criteria Decision Analysis. Springer, Belin (2005)zbMATHGoogle Scholar
  11. Knowles, J.D., Corne, D.: Local search, multiobjective optimization and the Pareto archived evolution strategy. In: Proceedings of Third Australia–Japan Joint Workshop on Intelligent and Evolutionary Systems, pp. 209–216 (1999)Google Scholar
  12. MedStar Health Cardiology Associates: Angioplasty/stenting. http://www.heartcapc.com/handler.cfm?event=practice,templatecpid=5916
  13. Patient Education Center: Coronary artery disease. http://www.patienteducationcenter.org/articles/coronary-artery-disease/
  14. Price, M.J.: Coronary Stenting: A Companion to Topol’s Textbook of Interventional Cardiology. Elsevier Health Sciences, Amsterdam (2013)Google Scholar
  15. Van Domburg, R., Foley, D., De Jaegere, P., De Feyter, P., Van den Brand, M., Van der Giessen, W., Hamburger, J., Serruys, P.: Long term outcome after coronary stent implantation: a 10 year single centre experience of 1000 patients. Heart 82(suppl 2), II27–II34 (1999)CrossRefGoogle Scholar
  16. Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective optimization with messy genetic algorithms. In: Proceedings of the 2000 ACM Symposium on Applied Computing, vol. 1, pp. 470–476. ACM (2000)Google Scholar
  17. Zitzler, E., Lothar, T.: An evolutionary algorithm for multiobjective optimization: the strength Pareto approach TIK-report 43 (1998).  https://doi.org/10.3929/ethz-a-004288833
  18. Zopounidis, C., Doumpos, M.: Multi-criteria decision aid in financial decision making: methodologies and literature review. J. Multi-Criteria Decis. Anal. 11(4–5), 167–186 (2002a)CrossRefzbMATHGoogle Scholar
  19. Zopounidis, C., Doumpos, M.: Multicriteria classification and sorting methods: a literature review. Eur. J. Oper. Res. 138(2), 229–246 (2002b)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial EngineeringMiddle East Technical UniversityAnkaraTurkey
  2. 2.Department of Industrial Engineering, Turkish Naval AcademyNational Defense UniversityIstanbulTurkey
  3. 3.Department of Cardiology, Medical FacultyOndokuz Mayıs UniversitySamsunTurkey

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