Analyzing Outcomes of Intrauterine Insemination Treatment by Application of Cluster Analysis or Kohonen Neural Networks

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Abstract

Intrauterine insemination (IUI) is one of many treatments provided to infertility patients. Many factors such as, but not limited to, quality of semen, the age of a woman, and reproductive hormone levels contribute to infertility. Therefore, the aim of our study is to establish a statistical probability concerning the prediction of which groups of patients have a very good or poor prognosis for pregnancy after IUI insemination. For that purpose, we compare the results of two analyses: Cluster Analysis and Kohonen Neural Networks. The k-means algorithm from the clustering methods was the best to use for selecting patients with a good prognosis but the Kohonen Neural Networks was better for selecting groups of patients with the lowest chances for pregnancy.

Ahmed, M. N., & Farag, A. A. (1997). Two-stage neural network for volume seg- mentation of medical images. Pattern Recognition Letters, 18, 1143-1151.

DeLapaz, R. L., Herskovits, E., Di Gesu, V., Hanson, W. J., & Bernstein, R. (1990). Cluster analysis of medical magnetic-resonance images data: diag- nostic application and evaluation. Proceedings of SPIE, 1259, Extracting Meaning from Complex Data: Processing, Display, Interaction, 176. DOI: 10.1117/12.19984.

Derwich, K., Jędrzejczak P., & Pawelczyk, L. (2008). Metody wspomaganego rozrodu. In Z. Słomko (Eds.), Ginekologia (pp. 516-532).Warszawa: PZWL.

Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the NationalAcademy of Sciences of the United States of America, 95(25), 14863-14868.

Horák, S. (2004). Insemination - indications, methods and efficiency. GinekologiaPraktyczna, 12(6), 41-49.

Kohonen, T. (1982). Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics, 43, 59-69.

Kohonen, T. (1995). Self-Organizing Maps. Springer.

Kurzawa, R., Kaniewska, D., & Bączkowski, T. (2010). Infertility from clinical and social perspective. Przewodnik Lekarza, 2, 149-152.

Licznar, P. & Łomotowski, J. (2006). Zastosowanie sieci neuronowych Kohonena do prognozowania dobowego poboru wody, Ochrona Środowiska, 28, 45-48.

McLachlan, G. J. (1992). Cluster analysis and related techniques in medical re- search. Statistical Methods in Medical Research, 1(1), 27-48.

Migut, G. (2009). Zastosowanie technik analizy skupień i drzew decyzyjnych do segmentacji rynku. StatSoft Polska. Retreived from: http://www.statsoft.pl/czytelnia/artykuly/Zastosowanietechnik.pdf.

Milewska, A. J., Gorska, U., Jankowska, D., Milewski, R., & Wołczyński, 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., & Wołczyński, S. (2012). Graphical representation of the relationships between qualitative variables concerning the process of hospitalization in the gynecological ward using correspondence analysis. Studies in Logic, Grammar and Rhetoric. Logical,Statistical and Computer Methods in Medicine, 29(42), 7-25.

Milewski, R., Jamiołkowski, J., Milewska, A. J., Domitrz, J., Szamatowicz, J., & Wołczyński, S. (2009). Prognosis of the IVF ICSI/ET procedure efficiency with the use of artificial neural networks among patients of the Depart- ment of Reproduction and Gynecological Endocrinology. Ginekologia Polska, 80(12), 900-906.

Milewski, R., Malinowski, P., Milewska, A. J., Czerniecki, J., Ziniewicz, P., & Wołczyński, 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., Pierzyński, P., & Wołczyński, 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., Leśniewska, M., & Wołczyński, S. (2013). Analysis of the demographic profile of patients treated for infertil- ity using assisted reproductive techniques in 2005-2010. Ginekologia Polska, 84(7), 609-614.

Milewski, R., Milewska, A. J., Domitrz, J., & Wołczyński, S. (2008). In vitro fer- tilization ICSI/ET in women over 40. Przegląd Menopauzalny, 7(2), 85-90. Pierzyński, P. (2011). Zajść w ciążę. Białystok: CMR.

Radwan, J. (2011). Badanie niepłodnej pary. In J. Radwan & S. Wołczyński (Eds.), Niepłodność i rozród wspomagany (pp. 47-66). Poznań: Termedia.

Radwan, J., Krasiński, R., & Gruszczyński,W. (2011). Badanie nasienia. In J. Rad- wan & S. Wołczyński (Eds.), Niepłodność i rozród wspomagany (pp. 67-80). Poznań: Termedia.

Radwan, P. (2011). Inseminacja domaciczna. In J. Radwan & S.Wołczyński (Eds.), Niepłodność i rozród wspomagany (pp. 165-178). Poznań: Termedia.

Sava, F. A., & Popa, R. I. (2011). Personality types based on the big five model. A cluster analysis over the Romanian population. Cognition, Brain, Behav-ior. An Interdisciplinary Journal, 15(3), 359-384.

Shannon, W., Culverhouse, R., & Duncan, J. (2003). Analyzing microarray data using cluster analysis. Future Medicine, 4(1), 41-52.

Stanisz, A. (2007). Przystępny kurs statystyki z zastosowaniem STATISTICA PLna przykładach z medycyny. T. 3. Analizy wielowymiarowe. Krakow: Stat- Soft.

Timm, N. T. (2002). Applied Multivariate Analysis, Springer.

Tkaczuk-Włach, J., Robak-Chołubek, D., & Jakiel, G. (2006). Male infertility. Przegląd Menopauzalny, 5, 333-338.

Wainer, R., Albert, M., Dorion, A., Bailly, M., Berge‘re, M., Lombroso, R., Gom- bault, M., & Selva, J. (2004). Influence of the number of motile spermatozoa inseminated and of their morphology on the success of intrauterine insemi- nation. Human Reproduction, 19(9), 2060-2065.

Xu, R., &Wunsch, D. C. 2nd. (2010). Clustering algorithms in biomedical research: a review. IEEE In Biomedical Engineering, 3, 120-154.

Studies in Logic, Grammar and Rhetoric

The Journal of University of Bialystok

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Cite Score 2017: 0.28

SCImago Journal Rank (SJR) 2017: 0.136
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