REVIEW OF FECUNDITY PREDICTION MODELS WITH RESPECT TO FERTILITY AND SUBFERTILITY MODELLING
DOI:
https://doi.org/10.33003/fjs-2025-0909-3738Keywords:
Fecundity, Fecundity prediction model, Subfertility, Deep learningAbstract
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.
References
Aguiar-Pérez, J. M., Pérez-Juárez, M. A., Alonso-Felipe, M., Del-Pozo-Velázquez, J., Rozada-Raneros, S., & Barrio-Conde, M. (2023). Understanding machine learning concepts. In Encyclopedia of data science and machine learning (pp. 1007-1022). IGI Global.https://doi.org/10.4018/978-1-7998-9220-5.ch058
Barrett J. C & Marshall, J. (1969). The risk of conception on different days of the menstrual cycle. Population studies. 1, 23(3), 455-61.
Barry, D. (1995), “A Bayesian model for growth curve analysis,” Biometrics, 51, 639-655. https://doi.org/10.2307/2532951
Colombo, B. and Masarotto, G. (2000). Daily fecundity: first results from a new data base. Demographic research, 3.https://doi.org/10.4054/DemRes.2000.3.5
Colombo, B., Mion, A., Passarin, K., & Scarpa, B. (2006). Cervical mucus symptom and daily fecundity: first results from a new database. Statistical Methods in Medical Research, 15(2), 161-180.https://doi.org/10.1191/0962280206sm437oa
Dewan, M., Mudgal, A., Pandey, P., Raghav, Y. Y., & Gupta, T. (2023). Predicting pregnancy complications using machine learning. In Technological Tools for Predicting Pregnancy Complications (pp. 141-160). IGI Global.https://doi.org/10.4018/979-8-3693-1718-1.ch008
Dunson, D. B. (2001). Bayesian modeling of the level and duration of fertility in the menstrual cycle. Biometrics, 57(4), 1067-1073.https://doi.org/10.1111/j.0006-341X.2001.01067.x
Dunson, D. B., Colombo, B., & Baird, D. D. (2002). Changes with age in the level and duration of fertility in the menstrual cycle. Human reproduction, 17(5), 1399-1403.https://doi.org/10.1093/humrep/17.5.1399
Dunson, D. B., & Colombo, B. (2003). Bayesian modeling of markers of day-specific fertility. Journal of the American Statistical Association, 98(461), 28-37.https://doi.org/10.1198/016214503388619067
Dunson, D. B., Baird, D. D., & Colombo, B. (2004). Increased infertility with age in men and women. Obstetrics & Gynecology, 103(1), 51-56.https://doi.org/10.1097/01.AOG.0000100153.24061.45
Dunson, D. B., & Stanford, J. B. (2005). Bayesian inferences on predictors of conception probabilities. Biometrics, 61(1), 126-133.https://doi.org/10.1111/j.0006-341X.2005.031231.x
Ecochard, R. (2006). Heterogeneity in fecundity studies: issues and modelling. Statistical methods in medical research, 15(2), 141-160.https://doi.org/10.1191/0962280206sm436oa
Ecochard, R., & Clayton, D. G. (2000). Multivariate parametric random effect regression models for fecundity studies. Biometrics, 56(4), 1023-1029.https://doi.org/10.1111/j.0006-341X.2000.01023.x
Fanton, M., Nutting, V., Solano, F., Maeder-York, P., Hariton, E., Barash, O., ...&Loewke, K. (2022). An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation. Fertility and Sterility, 118(1), 101-108.https://doi.org/10.1016/j.fertnstert.2022.04.003
Harville, D. A., &Mee, R. W. (1984). A mixed-model procedure for analyzing ordered categorical data. Biometrics, 393-408.https://doi.org/10.2307/2531393
Hougaard, P. (1986). Survival models for heterogeneous populations derived from stable distributions. Biometrika, 73(2), 387-396.https://doi.org/10.1093/biomet/73.2.387
Kassaw, E. A., Abate, B. B., Enyew, B. M., &Sendekie, A. K. (2025). The application of machine learning approaches to classify and predict fertility rate in Ethiopia. Scientific Reports, 15(1), 2562.https://doi.org/10.1038/s41598-025-85695-8
Kelsey, T. W., Hua, C. H., Wyatt, A., Indelicato, D., & Wallace, W. H. (2022). A predictive model of the effect of therapeutic radiation on the human ovary. Plos one, 17(11), e0277052.https://doi.org/10.1371/journal.pone.0277052
Kim, T. (2023). The impact of working hours on pregnancy intention in childbearing-age women in Korea, the country with the world’s lowest fertility rate. PloS one, 18(7), e0288697.https://doi.org/10.1371/journal.pone.0288697
Kim, S., Sundaram, R., & Buck Louis, G. M. (2010). Joint modeling of intercourse behavior and human fecundity using structural equation models. Biostatistics, 11(3), 559-571.https://doi.org/10.1093/biostatistics/kxq006
Kim, S., Sundaram, R., Louis, G. M. B., &Pyper, C. (2012). Flexible Bayesian human fecundity models. Bayesian analysis, 7(4), 771.https://doi.org/10.1214/12-ba726
Kim, S., Chen, Z., Perkins, N. J., Schisterman, E. F., & Louis, G. M. B. (2019). A Model-Based Approach to Detection Limits in Studying Environmental Exposures and Human Fecundity. Statistics in Biosciences, 11(3), 524-547. https://doi.org/10.1007/s12561-019-09243-5
Liu, B., Shi, S., Wu, Y., Thomas, D., Symul, L., Pierson, E., &Leskovec, J. (2019). Predicting pregnancy using large-scale datafrom a women's health tracking mobile application. In The World Wide Web Conference (pp. 2999-3005). ACM.https://doi.org/10.1145/3308558.3313512
Lum, K. J., Sundaram, R., Buck Louis, G. M., & Louis, T. A. (2016). A Bayesian joint model of menstrual cycle length and fecundity. Biometrics, 72(1), 193-203.https://doi.org/10.1111/biom.12379
Lum, K. J., Sundaram, R., Barr, D. B., Louis, T. A., & Louis, G. M. B. (2017). Perfluoroalkyl chemicals, menstrual cycle length, and fecundity: Findings from a prospective pregnancy study. Epidemiology (Cambridge, Mass.), 28(1), 90.https://doi.org/10.1097/EDE.0000000000000552
Malik, A., R., Shehu, M. A., Garba, S. and Audu. L. (2020). Machine Learning Model for Breast Cancer Detection. FUDMA Journal of Science. 4(1). pp. 55-61
McDonald, J. W., Rosina, A., Rizzi, E., & Colombo, B. (2011). Age and fertility: can women wait until their early thirties to try for a first birth?. Journal of biosocial science, 43(6), 685-700. https://doi.org/10.1017/S002193201100040X
Mbunge, E., Batani, J., Gaobotse, G., &Muchemwa, B. (2022). Virtual healthcare services and digital health technologies deployed during coronavirus disease 2019 (COVID-19) pandemic in South Africa: a systematic review. Global health journal, 6(2), 102-113.https://doi.org/10.1016/j.glohj.2022.03.001
Muhammad, A. S., Abdullahi, M. B., Abdulmalik, M. D., &Abisoye, O. A. (2023). User embedding long short-term model based fecundity prediction model using proposed fecundity dataset. East African Journal of Interdisciplinary Studies, 6(1), 37-53. https://doi.org/10.37284/eajis.6.1.1099
Muhammad, A. S., Abdullahi, M. B., Abdulmalik, M. D., & Abisoye, O. A. (2025). Enhancing Women's Fecundity Prediction in Time Series Data Using Encoder-LSTM Model Integration. SN Computer Science, 6(6), 641. https://doi.org/10.1007/s42979-025-04184-x
Naseem, S., Mahmood, T., Saba, T., Alamri, F. S., Bahaj, S. A. O., Ateeq, H., & Farooq, U. (2023). DeepFert: An intelligent fertility rate prediction approach for men based on deep learning neural networks. IEEE Access, 11, 75006-75022. https://doi.org/10.1109/ACCESS.2023.3290554
Pennoni, F., Barbato, M., & Del Zoppo, S. (2017). a latent Markov Model with covariates to study Unobserved heterogeneity among Fertility Patterns of couples employing natural Family Planning Methods. Frontiers in Public Health, 5, 186.https://doi.org/10.3389/fpubh.2017.00186
Ricks, T. N., Abbyad, C., &Polinard, E. (2022). Undoing racism and mitigating bias among healthcare professionals: lessons learned during a systematic review. Journal of racial and ethnic health disparities, 1-11.https://doi.org/10.1007/s40615-021-01137-x
Scarpa, B., & Dunson, D. B. (2007). Bayesian methods for searching for optimal rules for timing intercourse to achieve pregnancy. Statistics in medicine, 26(9), 1920-1936.https://doi.org/10.1002/sim.2846
Schwartz, D., MacDonald, P. D. M., &Heuchel, V. (1980). Fecundity, coital frequency and the viability of ova. Population Studies, 34(2), 397-400.https://doi.org/10.1080/00324728.1980.10410398
Shehu, M. A., Haruna, A., Jatto, A. A., & Hussein, U. (2018). An Adaptive Personnel Selection Expert System to Support Organization’s Personnel Recruitment Decision Process. I-Manager's Journal on Computer Science, 6(3).https://doi.org/10.26634/jcom.6.3.15700
Tarín, J. J., Pascual, E., García-Pérez, M. A., Gómez, R., Hidalgo-Mora, J. J., & Cano, A. (2020). A predictive model for women’s assisted fecundity before starting the first IVF/ICSI treatment cycle. Journal of Assisted Reproduction and Genetics, 37, 171-180.https://doi.org/10.1007/s10815-019-01642-3
Van der Steeg, J. W., Steures, P., Eijkemans, M. J., Habbema, J. D. F., Hompes, P. G., Broekmans, F. J., ... & Mol, B. W. (2006). Pregnancy is predictable: a large-scale prospective external validation of the prediction of spontaneous pregnancy in subfertile couples. Human reproduction, 22(2), 536-542.
Wang, C. W., Kuo, C. Y., Chen, C. H., Hsieh, Y. H., & Su, E. C. Y. (2022). Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. PLoS One, 17(6), e0267554.https://doi.org/10.1371/journal.pone.0267554
Yland, J.J., Wang, T., Zad, Z., Willis, S.K., Wang, T.R., Wesselink, A.K., Jiang, T., Hatch, E.E., Wise, L.A. and Paschalidis, I.C. (2022). Predictive models of pregnancy based on data from a preconception cohort study. Human Reproduction, 37(3), 565-576. https://doi.org/10.1093/humrep/deab280
Yu, J. L., Su, Y. F., Zhang, C., Jin, L., Lin, X. H., Chen, L. T., ...& Wu, Y. T. (2022). Tracking of menstrual cycles and prediction of the fertile window via measurements of basal body temperature and heart rate as well as machine-learning algorithms. Reproductive Biology and Endocrinology, 20(1), 118.https://doi.org/10.1186/s12958-022-00993-4
Zhan, Q., Zhao, J., Paziliya, Y., Zhao, J., La, X., & Yao, H. (2022). Establishing a predictive model for the evaluation of fecundity. Journal of Obstetrics and Gynaecology Research, 48(4), 987-1000. https://doi.org/10.1111/jog.15167
Zhu, X., Zhu, Z., Gu, L., Chen, L., Zhan, Y., Li, X., ...& Li, J. (2022). Prediction models and associated factors on the fertility behaviors of the floating population in China. Frontiers in public health, 10, 977103.https://doi.org/10.3389/fpubh.2022.977103
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2025 FUDMA JOURNAL OF SCIENCES

This work is licensed under a Creative Commons Attribution 4.0 International License.