Data Mining Approach for Predicting the Likelihood of Infertility in Nigerian Women

Introduction

The goal of data mining is to learn from data and the strategy used may vary as per the requirement (Yap et al., 2007). Data mining is an interdisciplinary field and is gaining popularity because of exploring Database technology, Information Science, Machine Learning and Neural Networks along with Statistical techniques. This study aims at applying data mining techniques to clinical data in order to predict the likelihood of infertility in Nigerian women.

Infertility is defined as one year of frequent, unprotected intercourse during which pregnancy has not occurred (Zegers-Hochschild et al., 2009). In another definition, infertility is the inability of a sexually active woman who is not practicing contraception to have a live birth (American Society for Reproductive Medicine (American Society for Reproductive Medicine (ASRM), 2008). Infertility can be attributed to any abnormality in the female or male reproductive system (European IVF-Monitoring Consortium, 2008). The etiology is mostly distributed fairly equally among the male and female with factors ranging from ovarian dysfunction, tubal factors amongst others. A smaller percentage of cases are attributed to endometriosis, uterine or cervical factors, or other causes. In approximately, one fourth of couples, the cause is uncertain and is referred to as unexplained infertility, while etiology is multifactorial for some couples (Puscheck & Woodad, 2009). Developed and developing societies tend to differ in prevailing assumptions about childlessness while in developed societies, voluntary childlessness is viewed as a more viable and legitimate option and women without children are often presumed to be voluntarily childfree (ASRM, 2006).

Infertility is a worldwide problem, affecting 8% – 15% of the couples in their reproductive age (Puscheck & Woodad, 2009; Boivin et al., 2009; Audu et al., 2003). WHO estimates that there are 60 - 80 million infertile couples worldwide with the highest incidence in some regions of Sub-Saharan Africa which is said to have an infertility belt warped around its center as infertility rate may reach 50% compared to 20% in Eastern Mediterranean Region and 11% in the developed world (Okonofua, 1996). In some parts of this belt, infertility is said to constitute up to 65% of gynecological consultations (Idrisa, 2005). Early exposures (e.g. in utero or in childhood) could permanently reprogram men and women for fecundity or biologic capacity (e.g. gynecologic and urologic health or gravid health during pregnancy) and fertility outcomes (e.g. multiple births or gestational age at delivery) which could affect adults later on set diseases (Skakkebaek et al., 2001). Thus, infertility could have public health implications beyond simply the inability to have children.

In general, an infertility evaluation is initiated after 12 months of unprotected intercourse during which pregnancy has not been achieved. Earlier investigation may be considered when historical factors, such as previous pelvic inflammatory disease or amenorrhea suggest infertility, although physicians should be aware that earlier evaluation may lead to unnecessary testing and treatment in some cases. Evaluation can be initiated earlier if the female partner is older than 35 years, because fertility rates decrease and spontaneous miscarriage and chromosomal abnormality rates increase with advancing maternal age (Puscheck & Woodad, 2009). Partners can be evaluated together or separately which can be due to the fact that each person may want to reveal information which their partner may be unaware of, such as previous pregnancy or sexually transmitted disease.