Evidence map›Paper›PMID 42517497›Full record

ArticleThe International journal of health planning and management2026

Machine Learning Analysis of Maternal Mortality Determinants in Nigeria: An Exploratory Study.

Abimbola Olaniran, Olumuyiwa Adesanya Ojo, Tope Olubodun, Maryam Al-Mujtaba, Linda Babalola, Grace Oloruntobi Olaniran, Abhinav Kothapalli, Ogochukwu Christiana Nwamata, Aduragbemi Banke-Thomas

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Article in The International journal of health planning and management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Abimbola OlaniranHealth Systems Strengthening, KIT Royal Tropical Institute, Amsterdam, the Netherlands.
Olumuyiwa Adesanya OjoHuman Resources for Health, World Health Organization-Nigeria, Abuja, Nigeria.
Tope OlubodunDepartment of Community Medicine and Primary Care, Federal Medical Centre Abeokuta, Abeokuta, Nigeria.
Maryam Al-MujtabaSchool of Nursing, Duke University, Durham, North Carolina, USA.
Linda BabalolaJudge Business School, University of Cambridge, Cambridge, UK.
Grace Oloruntobi OlaniranLagos State Health Service Commission, Lagos, Nigeria.
Abhinav KothapalliIssaquah High School, Issaquah, Washington, USA.
Ogochukwu Christiana NwamataDepartment of Computer and Information Science (CIS), Covenant University, Ota, Ogun State, Nigeria.
Aduragbemi Banke-ThomasLondon School of Hygiene and Tropical Medicine, University of London, London, UK.ORCID https://orcid.org/0000-0002-4449-0131

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNigeria confronts an ongoing maternal mortality crisis, accounting for 28.7% of global maternal deaths from 2.9% of the world's population. While proximate clinical causes are well characterised, the relative importance of distal socioeconomic and health system determinants remains poorly quantified, limiting evidence-informed prioritisation of scarce resources.

objectivesTo explore the predictive influence of individual, population, and health system factors on maternal mortality in Nigeria using machine learning approaches.

methodsThis exploratory ecological study used annual national-level data spanning Nigeria's independence to the most recently available dataset (1960-2024). XGBoost and Random Forest ensemble algorithms were applied to examine associations between multidimensional determinants and the maternal mortality ratio. Missing data were addressed using exponential triple smoothing, and model stability was assessed via 5-fold cross-validation.

resultsFemale tertiary education and service-sector employment showed the strongest and most stable associations with lower maternal mortality ratios. Early marriage, vulnerable employment, rural residence, and relative poverty were associated with a higher mortality ratio. Determinants of health-system financing were largely unstable; government per capita expenditure was associated with a lower maternal mortality ratio, whereas external funding dependency was associated with higher mortality ratio.

conclusionThis study challenges traditional perspectives on determinants of maternal mortality while highlighting key areas for policy intervention. Future research should examine the relationships between education, employment, and maternal mortality ratio through a longitudinal study to inform strategic investments.

Indexed as

Machine LearningMaternal MortalityBoosting Machine Learning AlgorithmsFemaleHumansNigeriaPrediction AlgorithmsPredictive Learning ModelsRandom ForestSocioeconomic FactorsAIhealthcare financingmachine learningmaternal mortalitysocial determinants of health

Identifiers

PMID42517497
PMCPMC13551501

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.