Comparison of Artificial Neural Networks and Logistic Regression Analysis in Pregnancy Prediction Using the In Vitro Fertilization Treatment

• Authors: Robert Milewski , Anna Justyna Milewska , Teresa Więsak , Allen Morgan
• Languages of publication: EN

Abstracts

EN

Infertility is recognized as a major problem of modern society. Assisted Reproductive Technology (ART) is the one of many available treatment options to cure infertility. However, the efficiency of the ART treatment is still inadequate. Therefore, the procedure’s quality is constantly improving and there is a need to determine statistical predictors as well as contributing factors to the successful treatment. There is a concern over the application of adequate statistical analysis to clinical data: should classic statistical methods be used or would it be more appropriate to apply advanced data mining technologies? By comparing two statistical models, Multivariable Logistic Regression analysis and Artificial Neural Network it has been demonstrated that Multivariable Logistic Regression analysis is more suitable for theoretical interest but the Artificial Neural Network method is more useful in clinical prediction.

• Publisher: Sciendo
• Journal: Studies in Logic, Grammar and Rhetoric
• Year: 2013
• Volume: 35
• Issue: 1
• Pages: 39-48

Dates

published

2013-12-01

online

2013-12-31

Contributors

author

Robert Milewski

• Department of Statistics and Medical Informatics, Medical University of Bialystok, Poland

author

Anna Justyna Milewska

• Department of Statistics and Medical Informatics, Medical University of Bialystok, Poland

author

Teresa Więsak

• Department of Gamete and Embryo Biology, Institute of Animal Reproduction and Food Research of Polish Academy of Sciences, Olsztyn, Poland

author

Allen Morgan

• Shore Institute for Reproductive Medicine, Lakewood, NJ, USA

References

• Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31.[Crossref]
• Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29-36.
• Hanley, J. A., & Hajian-Tilaki, K. O. (1997). Sampling variability of nonparametric estimates of the areas under receiver operating characteristic curves: An update. Academic Radiology, 4(1), 49-58.[Crossref][PubMed]
• Milewska, A. J., Gorska, U., Jankowska, D., Milewski, R., & Wolczynski, S. (2011). The use of the basket analysis in a research of the process of hospitalization in the gynecological ward. Studies in Logic, Grammar and Rhetoric. Logical, Statistical and Computer Methods in Medicine, 25(38), 83-98.
• Milewska, A. J., Jankowska, D., Gorska, U., Milewski, R., & Wolczynski, S. (2012). Graphical representation of the relationships between qualitative variables concerning the process of hospitalization in the gynaecological ward using correspondence analysis. Studies in Logic, Grammar and Rhetoric. Logical, Statistical and Computer Methods in Medicine, 29(42), 7-25.
• Milewski, R., Jamiolkowski, J., Milewska, A. J., Domitrz, J., Szamatowicz, J., & Wolczynski, S. (2009). Prognosis of the IVF ICSI/ET procedure efficiency with the use of artificial neural networks among patients of the Department of Reproduction and Gynecological Endocrinology. Ginekologia Polska, 80(12), 900-906.
• Milewski, R., Malinowski, P., Milewska, A. J., Czerniecki, J., Ziniewicz, P., & Wolczynski, S. (2011). Nearest neighbor concept in the study of IVF ICSI/ET treatment effectiveness. Studies in Logic, Grammar and Rhetoric. Logical, Statistical and Computer Methods in Medicine, 25(38), 49-57.
• Milewski, R., Malinowski, P., Milewska, A. J., Ziniewicz, P., Czerniecki, J., Pierzynski, P., &Wolczynski, S. (2012). Classification issue in the IVF ICSI/ET data analysis. Studies in Logic, Grammar and Rhetoric. Logical, Statistical and Computer Methods in Medicine, 29(42), 75-85.
• Milewski, R., Milewska, A. J., Czerniecki, J., Lesniewska, M., & Wolczynski, S. (2013). Analysis of the demographic profile of patients treated for infertility using assisted reproductive techniques in 2005-2010. Ginekologia Polska, 84(7), 609-614.
• Milewski, R., Milewska, A. J., Domitrz, J., & Wolczynski, S. (2008). In vitro fertilization ICSI/ET in women over 40. Przeglad Menopauzalny, 7(2), 85-90.
• Milewski, R., Pierzynski, P., Milewska, A. J., Zbucka-Kretowska, M., & Wolczynski, S. (2012). Concept of system allowing non invasive detection of uterine contractions in women undergoing in vitro fertilization - embryo transfer treatment. Studies in Logic, Grammar and Rhetoric. Logical, Statistical and Computer Methods in Medicine, 29(42), 99-106.
• Osowski, S. (2009). Artificial neural networks - basic network structures and learning algorithms. Przeglad Elektrotechniczny, 85(8), 1-8.
• Radwan, J. (2011). Epidemiologia nieplodnosci. In J. Radwan, & S. Wolczynski (Eds.), Nieplodnosc i rozrod wspomagany (pp. 11-14). Poznan: Termedia.
• Siristatidis, C., Pouliakis, A., Chrelias, C., & Kassanos, D. (2011). Artificial intelligence in IVF: A need. Systems Biology in Reproductive Medicine, 57(4), 179-185.[WoS]
• Tadeusiewicz, R. (1993). Sieci neuronowe.Warszawa: Akademicka OficynaWydawnicza RM.
• Thoma, M. E., McLain, A. C., Louis, J. F., King, R. B., Trumble, A. C., Sundaram, R., & Buck Louis, G. M. (2013). Prevalence of infertility in the united states as estimated by the current duration approach and a traditional constructed approach. Fertility and Sterility, 99(5), 1324-1331e1.[WoS][Crossref]
• Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques (3rd ed.). San Francisco, CA, USA: Morgan Kaufmann.

Identifiers

DOI

10.2478/slgr-2013-0033