References
Aborode, A. T., Otorkpa, O. J., Abdullateef, A. O., Oluwaseun, O. S., Adegoye, G. A., Aondongu, N. J., & Komakech, J. J. (2025). Impact of Climate Change-Induced Flooding Water Related Diseases and Malnutrition in Borno State, Nigeria: A Public Health Crisis. Environmental Health Insights, 19, 11786302251321683 Amofa, S. G. (2024). Building a Predictive Model on Maternal Mortality Using Machine Learning: Comparison of Different Modelling Techniques (Doctoral dissertation, University of Cape Coast). East, U. M., & Region, N. A. (2024). Nutrition Strategic Direction 2030. Gee, S., Vargas, J., & Foster, A. M. (2019). The more children you have, the more praise you get from the community: exploring the role of sociocultural context and perceptions of care on maternal and newborn health among Somali refugees in UNHCR supported camps in Kenya. Conflict and health, 13(1), 11. Ezeh, O. K., Ogbo, F. A., Odumegwu, A. O., Oforkansi, G. H., Abada, U. D., Goson, P. C., ... & Agho, K. E. (2021). Under-5 mortality and its associated factors in Northern Nigeria: evidence from 22,455 Singleton Live Births (2013–2018). International Journal of Environmental Research and Public Health, 18(18), 9899. Kalayou, M. H., Kassaw, A. A. K., & Shiferaw, K. B. (2024). Empowering child health: Harnessing machine learning to predict acute respiratory infections in Ethiopian under- fives using demographic and health survey insights. BMC Infectious Diseases, 24(1), 338 Kayode, G. A., Adekanmbi, V. T., & Uthman, O. A. (2012). Risk factors and a predictive model for under-five mortality in Nigeria: Evidence from NDHS. BMC Pregnancy and Childbirth, 12(1), 10. Margret, I. N., Rajakumar, K., Arulalan, K. V., & Manikandan, S. (2024). Statistical insights into machine learning-based box models for pregnancy care and maternal mortality reduction: A literature survey. IEEE Access, 12, 68184-68207. Mohammed, F. (2022). Displacement to the camp vs Displacement to the city: A comparative study of Internally Displaced Persons capabilities in Maiduguri, Borno State, Northeastern Nigeria (Doctoral dissertation, University of Sheffield). Naznin, S., Uddin, M. J., Ahmad, I., & Kabir, A. (2025). Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. PloS one, 20(2), e0317715. Ogbuoji, O., & Yamey, G. (2019). How many child deaths can be averted in Nigeria? Assessing state level prospects of achieving 2030 sustainable development goals for neonatal and under-five mortality. Gates Open Research, 3, 1460. Ojewumi, T. K., & Ojewumi, J. S. (2012). Trends in Infant and Child Mortality in Nigeria: a wake- up call assessment for intervention towards achieving the 2015 MDGs. Science Journal of sociology and Anthropology, 3, 1-10. Rahman, A., & Rahman, M. H. (2025). Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh. BMC Public Health, 25(1), 360. Saroj, R. K., Yadav, P. K., Singh, R., et al. (2022). Machine learning algorithms for understanding the determinants of under-five mortality. BioData Mining, 15(20). E- ISSN 2489-009X , Shukla, V. V., Eggleston, B., Ambalavanan, N., McClure, E. M., Mwenechanya, M., Chomba, E., & Carlo, W. A. (2020). Predictive modeling for perinatal mortality in resource-limited settings. JAMA network open, 3(11), e2026750. Silva, G. F. D. S., Wichmann, R. M., da Silva Junior, F. C., & Chiavegatto Filho, A. D. P. (2025). Development and evaluation of machine learning training strategies for neonatal mortality. Tessema, Z. T., & Zeleke, T. A. (2020). Individual and community-level determinants of childhood mortality in Ethiopia using Bayesian multilevel analysis. BMC Pediatrics, 20(1), 1 12. Veloso, F. C., Barros, C. R., Kassar, S. B., & Gurgel, R. Q. (2024). Neonatal death prediction scores: a systematic review and meta-analysis. BMJ paediatrics open, 8(1), e003067. Young, H., & Marshak, A. (2017). Persistent global acute malnutrition. Feinstein Int Cent Publ, 55.