Abstract:
Infertility is a worldwide problem, affecting 8% – 15% of the couples in their reproductive age. WHO estimates
that there are 60 - 80 million infertile couples worldwide with the highest incidence in some regions of SubSaharan Africa also infertility rate may reach 50% compared to 20% in Eastern Mediterranean Region and 11%
in the developed world. Infertility has caused considerable social, emotional and psychological stress between
couples, among families, within the individual concerned and the society at large. Historical data constituting
information describing the risk factors of infertility alongside the respective infertility likelihood status of women
was collected from Obafemi Awolowo University Teaching Hospital Complex (OAUTHC). The predictive model
was formulated using naïve Bayes’, decision trees and multi-layer perceptron algorithm – supervised machine
learning algorithms. The formulated model was simulated using the Waikato Environment for Knowledge
Analysis (WEKA) environment. The results of the performance evaluation of the machine learning algorithms
showed that the C4.5 decision trees and the multi-layer perceptron with an accuracy of 74.4% each
outperformed the naïve Bayes’ algorithm. In addition, the decision trees algorithm recognized variables relevant
to predicting infertility and a rule that can be applied on patient risk factor records for infertility likelihood
prediction was deduced from the tree structure. This showed how effective machine learning algorithms can be
used in predicting the likelihood of infertility in Nigerian women.
