Abstract
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.
Highlights
Infertility refers to an inability to conceive after having regular unprotected sex for a period of at least one year (Radwan J., 2011)
Segmentation methods such as Cluster Analysis or Kohonen Neural Networks were applied because they provide an option for uniformly grouping data to determine/estimate percentages of successful pregnancies
In spite of male factor presence, all the semen parameters were above the median and 61% of patients were stimulated with gonadotropins
Summary
Infertility refers to an inability to conceive after having regular unprotected sex for a period of at least one year (Radwan J., 2011). The female, male or both partners can contribute to the couple’s infertility. It has been estimated that in approximately 20–30% of couples, both partners suffer from infertility (Kurzawa et al, 2010). A study conducted by the World Health Organization showed that males might contribute in 50% to these couples’ infertility (Radwan et al, 2011). At the beginning of any treatment, the male and female are evaluated to establish the reason(s) of infertility. Many tests need to be performed to establish a diagnosis for the couple. The potency of the fallopian tubes and uterus, and concentrations of repro-
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