Socioeconomic, Nutritional, and Demographic Determinants of Anaemia among Nigerian Women: A Machine Learning Analysis of DHS 2024 Data

Abstract

Anaemia remains a serious public health problem among women of reproductive age in Nigeria, with significant implications for maternal and population health. This study examined the prevalence, determinants, and predictability of anaemia using descriptive statistics, inferential analyses, and supervised machine-learning models on the data from the 2023–2024 Nigeria Demographic and Health Survey (NDHS). Anaemia status was defined using World Health Organisation haemoglobin thresholds. The results indicate an alarming 78% prevalence of anaemia among Nigerian women across geographic, socioeconomic, and educational strata. Nutritional status, particularly body mass index, together with reproductive factors and contextual characteristics, emerged as the most influential predictors of anaemia. The study highlights the value of combining epidemiological analysis with interpretable machine learning to inform targeted strategies for anaemia prevention and control in Nigeria.