Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare

Mathematical Modeling in Reproduction and Infertility

Author(s): Nojan Hafizi* and Pinar Tulay

Pp: 214-234 (21)

DOI: 10.2174/9781681088716121010014

* (Excluding Mailing and Handling)


Infertility is a major concern in health sciences. Many treatment strategies are being developed to overcome this problem. Assisted reproductive technologies (ART) are used in the treatment of infertility. Depending on the cause of infertility, different approaches can be applied in the field of ART treatments. However, since infertility is complex, it may be difficult to select the best treatment strategy for each patient. Mathematical modeling is used to understand better and sometimes even predict a pattern and outcome of biological processes. Thus, developing models help scientists and medical doctors select the best way of treatment and improve pregnancy rates. To date, a number of mathematical model systems have been tested to classify different parameters in infertile patients to develop a model that can predict the chances of becoming pregnant by identifying the behavioral design. In this chapter, several mathematical models are reviewed that corroborate the data obtained from infertile patients and predict the outcome depending on different parameters, including female age, follicle size, and hormonal levels.

Keywords: Assisted Reproductive Technologies (ART), Fuzzy Logic, Hormonal Levels, Infertility, Mathematical Modeling, Prediction in Pregnancy Rate, Pregnancy Calculation, Reproduction.

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