Advancing Maternal and Child Health in Nigeria through Support Vector Machines
Keywords:
Maternal and Child Health, Machine Learning, Support Vector Machines, Predictive Analytics, Healthcare Optimization, Nigeria
Abstract
The alarming rate of maternal and neonates’ morbidity and mortality rates in the country prompted a genuine commitment to enhance the health and well-being of mothers and children. This research aims to address the critical health challenges that causes maternal and neonates mortality using SVM to improve health outcomes, optimize healthcare efficiency and address existing disparity. SVM, a supervised machine learning algorithm, is employed to predict risks during pregnancy and childbirth by analyzing health indicators such as blood pressure, age, and heart rate so as to develop a predictive model to identify high-risk cases early and support healthcare professionals in implementing timely interventions.
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Akeju, D., Okusanya, B., Okunade, K., Ajepe, A., Allsop, M., &Ebenso, B. (2022). Sustainability of the effects and impacts of using digital technology to extend maternal health services to rural and hard-to-reach populations: Experience from Southwest Nigeria. Frontiers in Global Women’s Health.
Amose, J., Manimegalai, P., Scholar, R., &Narmatha, C. (2022). Comparative performance analysis of kernel functions in support vector machines in the diagnosis of pneumonia using lung sounds. 2022 2nd International Conference on Computing and Information
Technology (ICCIT).
Appel-Rubio, J., Aguayo, C., Damiano, A. E., Guzmán-Gutiérrez, E., & Araya, J. (2023). Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications. Frontiers in Endocrinology.
Aswini, J., Yamini, B., Jatothu, R., Nayaki, K. S., &Nalini, M. (2021). An efficient cloud‐based healthcare services paradigm for chronic kidney disease prediction application using boosted support vector machine. Concurrency and Computation: Practice and Experience.
Azuogu, B., Akamike, I., Okedo-Alex, I., Adeke, A., Agu, A., Akpa, C., Obiechina, N., Akpa, W., Nwali, D., & Anyigor, C. (2020). Knowledge, attitude, and perceived partner and socio-cultural support for family planning among women of reproductive age in a rural community in Ebonyi State, Nigeria.
Badillo, S., Banfai, B., Birzele, F., Davydov, I. I., Hutchinson, L., Kam‐Thong, T., & Zhang, J. D. (2020). An introduction to machine learning. Clinical Pharmacology & Therapeutics, 107(4), 871–885.
Bukar, L. (2019). The National Health Insurance Scheme: An alternative source of healthcare funding in Nigeria—A case study of Borno State. Texila International Journal of Management.
Dahab, R., &Sakellariou, D. (2020). Barriers to accessing maternal care in low-income countries in Africa: A systematic review. International Journal of Environmental Research and Public Health.
Dixon, D., Sattar, H., Moros, N., Kesireddy, S. R., Ahsan, H., Lakkimsetti, M., Fatima, M., Doshi, D., Sadhu, K., &Junaid Hassan, M. (2024). Unveiling the influence of AI predictive analytics on patient outcomes: A comprehensive narrative review.
Ekpenyong, B., Agbaje, E., & Omotoye, J. (2019). Healthcare delivery in Nigeria: Addressing maternal and child mortality. African Health Sciences, 19(4), 3024–3032.
Feldner-Busztin, D., Nisantzis, P. F., Edmunds, S. J., Boza, G., Racimo, F., Gopalakrishnan, S., Limborg, M., Lahti, L., & Polavieja, G. G. de. (2023). Dealing with dimensionality:
The application of machine learning to multi-omics data. Bioinformatics.
ForeSee Medical. (2024). Predictive analytics in healthcare: Explore benefits & applications.
Habehh, H., &Gohel, S. (2021). Machine learning in healthcare. Current Genomics, 22(4), 291.
Mennickent, D., Rodríguez, A., Opazo, M. C., Riedel, C. A., Castro, E., &Eriz-Salinas, A. (2022). What is machine learning: Definition, types, applications and examples. Potentia Analytics.
Harahap, T. H., Mansouri, S., Abdullah, O. S., Uinarni, H., Askar, S., Jabbar, T. L., Alawadi, A. H., & Hassan, A. Y. (2024). An artificial intelligence approach to predict infants’ health status at birth. International Journal of Medical Informatics.
IBM. (2024). What is support vector machine? IBM.
Iqbal, F., Satti, M. I., Irshad, A., & Shah, M. A. (2023). Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach. Open Life Sciences.
Lanza, B., & Parashar, D. (2020). Do support vector machines play a role in stratifying patient population based on cancer biomarkers? Archives of Proteomics and Bioinformatics.
Mlandu, C., Matsena-Zingoni, Z., & Musenge, E. (2023). Predicting the dropout from the maternal, newborn, and child healthcare continuum in three East African Community countries: Application of machine learning models. BMC Medical Informatics and Decision Making.
Ngusie, H., Mengiste, S., Zemariam, A., & Molla, B. (2024). Predicting adverse birth outcomes among childbearing women in Sub-Saharan Africa: Employing innovative machine learning techniques. BMC Public Health.
Nigeria Health Watch. (2024). An unseen grief: Maternal mortality’s impact on infants and children. Medium.
Obasohan, P. E., Gana, P., Mustapha, M. A., Umar, A. E., Makada, A., & Obasohan, D. N. (2019). Decision-making autonomy and maternal healthcare utilization among Nigerian women. International Journal of Maternal and Child Health and AIDS (IJMA).
Ogu, U. U., et al. (2023). Demand and supply analysis for maternal and child health services at the primary healthcare level in Nigeria. BMC Health Services Research.
Ope, B. W. (2020). Reducing maternal mortality in Nigeria: Addressing maternal health services’ perception and experience. Journal of Global Health Reports.
Olonade, O., Olawande, T. I., & Alabi, O. J. (2019). Maternal mortality and maternal healthcare in Nigeria: Implications for socioeconomic development.
Petrova, B. (2024). Predictive analytics in healthcare | Reveal. Reveal Embedded Analytics.
Samuel, O., Zewotir, T., & North, D. (2024). Application of machine learning methods for predicting under-five mortality: Analysis of Nigerian demographic health survey 2018 dataset. BMC Medical Informatics and Decision Making.
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications, and research directions. SN Computer Science, 2(3), 160.
Mao, W., & Watkins, D. (2023). Effects of public financing of essential maternal and child health interventions across wealth quintiles in Nigeria: An extended cost-effectiveness analysis.
Yarasuri, V. K., Reddy, D. S., Muneesh, P. S., Kaushik, R. V. S., Vardhan, T. N., & Nisha, K. L. (2022). Developing machine learning models for cardiovascular disease prediction. 2022 2nd Asian Conference on Innovation in Technology.
Published
2026-03-29
Section
Articles
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