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Soft Computing

, Volume 22, Issue 1, pp 79–90 | Cite as

Behavior patterns in hormonal treatments using fuzzy logic models

  • J. G. EnríquezEmail author
  • V. Cid
  • N. Muntaner
  • J. Aroba
  • J. Navarro
  • F. J. Domínguez-Mayo
  • M. J. Escalona
  • I. Ramos
Methodologies and Application

Abstract

Assisted reproductive technologies are a combination of medical strategies designed to treat infertility patients. Ideal stimulation treatment has to be individualized, but one of the main challenges which clinicians face in the everyday clinic is how to select the best medical protocol for a patient. This work aims to look for behavior patterns in this kind of treatments, using fuzzy logic models with the objective of helping gynecologists and embryologists to make decisions that could improve the process of in vitro fertilization. For this purpose, a real-world dataset composed of one hundred and twenty-three (123) patients and five hundred and fifty-nine (559) treatments applied in relation to such patients provided by an assisted reproduction clinic, has been used to obtain the fuzzy models. As conclusion, this work corroborates some known clinic experiences, provides some new ones and proposes a set of questions to be solved in future experiments.

Keywords

Hormonal treatments IVF Fuzzy logic Data mining 

Notes

Acknowledgements

This research has been supported by the MeGUS project (TIN2013-46928-C3-3-R), Pololas project (TIN2016-76956-C3-2-R), by the SoftPLM Network (TIN2015-71938-REDT) of the Spanish the Ministry of Economy and Competitiveness and Fujitsu Laboratories of Europe (FLE).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interests.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Aroba J (2003) Advances in the decision making in software development projects. Ph.D. thesis. University of SevilleGoogle Scholar
  2. Aroba J, Cuadrado-Gallego JJ, Sicilia M-Á, Ramos I, García-Barriocanal E (2008) Segmented software cost estimation models based on fuzzy clustering. J Syst Softw 81:1944–1950CrossRefGoogle Scholar
  3. Borges G, Brusamarello V (2015) Sensor fusion methods for reducing false alarms in heart rate monitoring. J Clin Monit Comput 30(6):859–867Google Scholar
  4. Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40CrossRefGoogle Scholar
  5. Cotton S, Hill S, Hirstein AF, James JL (2005) Model assisted reproductive technology act. J Gend Race Justice 9:55Google Scholar
  6. Fukuyama Y, Sugeno M (1989) A new method of choosing the number of clusters for fuzzy means method. In: Proceedings of the 5th fuzzy systems symposium, pp 247–250Google Scholar
  7. Furuhashi T, Suzuki T (2001) On interpretability of fuzzy models based on conciseness measure. In: The 10th IEEE international conference on fuzzy systems, 2001, pp 284–287Google Scholar
  8. Gegúndez ME, Aroba J, Bravo JM (2008) Identification of piecewise affine systems by means of fuzzy clustering and competitive learning. Eng Appl Artif Intell 21:1321–1329CrossRefGoogle Scholar
  9. Glymour C, Madigan D, Pregibon D, Smyth P (1996) Statistical inference and data mining. Commun ACM 39:35–41CrossRefGoogle Scholar
  10. Guillaume S (2001) Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Trans Fuzzy Syst 9:426–443CrossRefGoogle Scholar
  11. Hand DJ (1998) Data mining: statistics and more? Am Stat 52:112–118Google Scholar
  12. Hathaway RJ, Bezdek JC (1993) Switching regression models and fuzzy clustering. IEEE Trans Fuzzy Syst 1:195–204CrossRefGoogle Scholar
  13. Hoppner F, Klawonn F (2003) A contribution to convergence theory of fuzzy c-means and derivatives. IEEE Trans Fuzzy Syst 11(5):682–694CrossRefGoogle Scholar
  14. Josefiok M, Sauer J (2015) Towards an expert system for the field of neurology based on fuzzy logic. In: Joint German/Austrian conference on artificial intelligence (Knstliche Intelligenz), pp 333–340Google Scholar
  15. Jurisica I, Mylopoulos J, Glasgow J, Shapiro H, Casper RF (1998) Case-based reasoning in IVF: prediction and knowledge mining. Artif Intell Med 12:1–24. doi: 10.1016/S0933-3657(97)00037-7 CrossRefGoogle Scholar
  16. Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. Wiley, New YorkzbMATHGoogle Scholar
  17. Lawson AK, Klock SC, Pavone ME, Hirshfeld-Cytron J, Smith KN, Kazer RR (2014) Prospective study of depression and anxiety in female fertility preservation and infertility patients. Fertil Steril 102:1377–1384. doi: 10.1016/j.fertnstert.2014.07.765
  18. Lopes MHBM, DAncona CAL, Ortega NRS, Silveira PSP, Faleiros-Martins AC, Marin HF (2015) A fuzzy logic model for differential diagnosis of lower urinary tract dysfunctions. Int J Urol Nurs 10(3):146–153Google Scholar
  19. Miyahira SA, Araujo E (2008) Fuzzy obesity index for obesity treatment and surgical indication. In: IEEE international conference on fuzzy systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence), pp 2392–2397Google Scholar
  20. Morales DA, Bengoetxea E, Larraaga P, García M, Franco Y, Fresnada M, Merino M (2008) Bayesian classification for the selection of in vitro human embryos using morphological and clinical data. Comput Methods Programs Biomed 90:104–116. doi: 10.1016/j.cmpb.2007.11.018 CrossRefGoogle Scholar
  21. Nakajima H (1996) Fuzzy logic and data mining. In: Proceedings of the 1996 Asian fuzzy systems, IEEE 1996, pp 133–138Google Scholar
  22. Panavaranan P, Wongsawat Y (2013) EEG-based pain estimation via fuzzy logic and polynomial kernel support vector machine. In: Biomedical engineering international conference (BMEiCON), 2013 6th, pp 1–4Google Scholar
  23. Rane AL (2015) Clinical decision support model for prevailing diseases to improve human life survivability. In: 2015 international conference on pervasive computing (ICPC), pp 1–5Google Scholar
  24. SEF (2016) Sociedad Espaola de Fertilidad. Accessed Oct 2016Google Scholar
  25. Shamim MS, Enam SA, Qidwai U (2009) Fuzzy logic in neurosurgery: predicting poor outcomes after lumbar disk surgery in 501 consecutive patients. Surg Neurol 72:565–572CrossRefGoogle Scholar
  26. Sugeno M, Yasukawa T (1993) A fuzzy-logic-based approach to qualitative modeling. IEEE Trans Fuzzy Syst 1:7–31. doi: 10.1109/TFUZZ.1993.390281 CrossRefGoogle Scholar
  27. Tartarisco G, Baldus G, Corda D, Raso R, Arnao A, Ferro M, Gaggioli A, Pioggia G (2012) Personal health system architecture for stress monitoring and support to clinical decisions. Comput Commun 35:1296–1305CrossRefGoogle Scholar
  28. Yilmaz A, Ayan K (2011) Risk analysis in breast cancer disease by using fuzzy logic and effects of stress level on cancer risk. Sci Res Essays 6:5179–5191Google Scholar
  29. Yilmaz A, Ayan K (2013) Cancer risk analysis by fuzzy logic approach and performance status of the model. Turk J Electr Eng Comput Sci 21:897–912Google Scholar
  30. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Computer Languages and SystemsUniversity of SevilleSevilleSpain
  2. 2.Department of Information TechnologiesUniversity of HuelvaHuelvaSpain
  3. 3.INEBIRHospital Victoria EugeniaSevilleSpain

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