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Machine Learning Analysis of Maternal Mortality Determinants in Nigeria: An Exploratory Study
Abimbola Olaniran1, Olumuyiwa Adesanya Ojo2, Tope Olubodun3
1Health Systems Strengthening, KIT Royal Tropical Institute, Amsterdam, the Netherlands.
Summary
Higher female education and service sector jobs significantly reduce maternal mortality in Nigeria. Conversely, early marriage, poverty, and rural living increase risks, underscoring the need for targeted policy interventions.
Area of Science:
- Public Health
- Demography
- Health Economics
Background:
- Nigeria faces a severe maternal mortality crisis, contributing disproportionately to global deaths.
- Socioeconomic and health system factors influencing maternal deaths require better quantification for resource allocation.
Purpose of the Study:
- To investigate the predictive power of individual, population, and health system factors on maternal mortality in Nigeria.
- Utilize machine learning to analyze complex determinants of maternal mortality ratio.
Main Methods:
- Ecological study using national data from 1960-2024.
- Applied XGBoost and Random Forest algorithms to assess determinant associations.
- Employed exponential triple smoothing for missing data and 5-fold cross-validation for model stability.
Main Results:
- Female tertiary education and service-sector employment strongly correlated with reduced maternal mortality.
- Early marriage, vulnerable employment, rural residence, and poverty linked to increased maternal mortality.
- Health system financing determinants showed instability; government spending beneficial, external funding dependency detrimental.
Conclusions:
- Findings challenge traditional views on maternal mortality determinants.
- Emphasize education and employment as key policy intervention areas.
- Recommend longitudinal studies to further elucidate education, employment, and maternal mortality relationships.
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