References
Abdulwahab, S., Askar, S., & Hussien, D. (2025). Comprehensive Review of Advanced Machine Learning Strategies for Resource Allocation in Fog Computing Systems. The Indonesian Journal of Computer Science, 14(1). Adedokun, S. T., & Uthman, O. A. (2019). Women who have not utilized health Service for Delivery in Nigeria: who are they and where do they live?. BMC pregnancy and childbirth, 19(1), 93. Ademuyiwa, I. Y., Opeke, R. O., Farotimi, A. A., Ejidokun, A., Olowe, A. O., & Ojo, E. A. (2021). Awareness and satisfaction with antenatal care services among pregnant women in Lagos state, Nigeria. Akinyemi, A. I., Ikuteyijo, O. O., Mobolaji, J. W., Erinfolami, T., & Adebayo, S. O. (2022). Socioeconomic inequalities and family planning utilization among female adolescents in urban slums in Nigeria. Frontiers in Global Women's Health, 3, 838977. Alkema, L., Chou, D., Hogan, D., Zhang, S., Moller, A. B., Gemmill, A., ... & Say, L. (2016). Global, regional, and national levels and trends in maternal mortality between 1990 and 2015, with scenario-based projections to 2030: a systematic analysis by the UN Maternal Mortality Estimation Inter-Agency Group. The lancet, 387(10017), 462-474. Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. Jama, 319(13), 1317-1318. De Souza, M. J., Nattiv, A., Joy, E., Misra, M., Williams, N. I., Mallinson, R. J., ... & Matheson, G. (2014). 2014 female athlete triad coalition consensus statement on treatment and return to play of the female athlete triad: 1st International Conference held in San Francisco, California, May 2012 and 2nd International Conference held in Indianapolis, Indiana, May 2013. British journal of sports medicine, 48(4), 289-289. Khan, S. A. R., Zia?ul?haq, H. M., Umar, M., & Yu, Z. (2021). Digital technology and circular economy practices: An strategy to improve organizational performance. Business Strategy & Development, 4(4), 482-490. Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., ... & Bolton, E. E. (2021). PubChem in 2021: new data content and improved web interfaces. Nucleic acids research, 49(D1), D1388-D1395. Knight, M. (2019). The findings of the MBRRACE-UK confidential enquiry into maternal deaths and morbidity. Obstetrics, Gynaecology & Reproductive Medicine, 29(1), 21-23. Moons, K. G., Altman, D. G., Reitsma, J. B., Ioannidis, J. P., Macaskill, P., Steyerberg, E. W., ... & Collins, G. S. (2015). Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis : explanation and elaboration. Annals of internal medicine, 162(1), W1-W73. Nair, M., Chhabra, S., Choudhury, S. S., Deka, D., Deka, G., Kakoty, S. D., ... & Kurinczuk, J. J. (2021). Relationship between anaemia, coagulation parameters during pregnancy and postpartum haemorrhage at childbirth: a prospective cohort study. BMJ open, 11(10), e050815. Nigeria Population Commission. (2019). Nigeria demographic and health survey 2018. NPC, ICF. Okonofua, F., Imosemi, D., Igboin, B., Adeyemi, A., Chibuko, C., Idowu, A., & Imongan, W. (2017). Maternal death review and outcomes: An assessment in Lagos State, Nigeria. PloS one, 12(12), e0188392. Ozdenerol, E. (2016). Spatial health inequalities: Adapting GIS tools and data analysis. CRC Press. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372. Sani, J., Oluwagbemiga, A., & Ahmed, M. M. (2025). Machine Learning-Based Prediction of Optimal Antenatal Care Utilization among Reproductive Women in Nigeria. Machine Learning with Applications, 100698. Sendak, M., Elish, M. C., Gao, M., Futoma, J., Ratliff, W., Nichols, M., ... & O'Brien, C. (2020, January). ' The human body is a black box' supporting clinical decision-making with deep learning. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 99-109). Taye, E. A., Woubet, E. Y., Hailie, G. Y., Arage, F. G., Zerihun, T. E., Zegeye, A. T., ... & Kassaw, A. T. (2025). Application of the random forest algorithm to predict skilled birth attendance and identify determinants among reproductive-age women in 27 Sub-Saharan African countries; machine learning analysis. BMC Public Health, 25(1), 901. Thaddeus, S., & Maine, D. (1994). Too far to walk: maternal mortality in context. Social science & medicine, 38(8), 1091-1110. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44-56. Vayena et al., 2018 Vayena, E., Haeusermann, T., Adjekum, A., & Blasimme, A. (2018). Digital health: meeting the ethical and policy challenges. Swiss medical weekly, 148, w14571. Walle, A. D., Kebede, S. D., Adem, J. B., & Mamo, D. N. (2025). Machine-learning algorithm to predict home delivery after antenatal care visit among reproductive age women in East Africa. Frontiers in Global Women’s Health, 6, 1461475. Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., ... & Goldenberg, A. (2019). Do no harm: a roadmap for responsible machine learning for health care. Nature medicine, 25(9), 1337-1340. Wolff, R. F., Moons, K. G., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., ... & PROBAST Group†. (2019). PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Annals of internal medicine, 170(1), 51-58. World Health Organization. (2019). World health statistics overview 2019: monitoring health for the SDGs, sustainable development goals (No. WHO/DAD/2019.1). World Health Organization. World Health Organization. (2023). Global report on neglected tropical diseases 2023. World Health Organization. Yehuala, T. Z., Mengesha, S. B., & Baykemagn, N. D. (2025). Predicting pregnancy loss and its determinants among reproductive-aged women using supervised machine learning algorithms in Sub-Saharan Africa. Frontiers in Global Women's Health, 6, 1456238.