REVIEW OF FECUNDITY PREDICTION MODELS WITH RESPECT TO FERTILITY AND SUBFERTILITY MODELLING - Muhammad, Ahmad Shehu (FUDMA JOURNAL OF SCIENCES, 2025)
Couples understanding their respective fecundity gives the opportunity for keeping track of their fertility status and thus helps to know if and when medical intervention is needed or not. To help understand couples fecundity, fecundity prediction models were developed using statistical/machine/deep learning models. Fecundity prediction models are developed with the possible need for improvements or advancements, and to identify the improvements and advancements with respect to analyzing fecundity heterogeneities among fertile and sub fertile couples, the models from 2000 to 2025 are reviewed. In reviewing existing models for fecundity studies, the models were further categorized from the existing categories, and each fecundity models category were reviewed against the fertility and subfertility definitions (which are applicable to fertile and subfertile couples respectively). Based on the review outcome, it was observed that assumptions used for developing most models for analyzing subfertility heterogeneities in each models category may deny the models from achieving satisfactory conclusive analysis on fecundity heterogeneities among couples. Also, existing models does not explicitly distinguish fertility and subfertility during fecundity analysis.
Keywords: Fecundity; Fecundity prediction model; Subfertility; Deep learning