Predicting Childhood Anaemia in Nigeria: A Machine Learning Approach to Uncover Key Risk Factors

Background : Childhood anaemia remains a significant public health challenge, particularly in low- and middle-income countries like Nigeria, where the prevalence among children under five is alarmingly high. This study aims to identify the key determinants of childhood anaemia, develop an accurate predictive model using advanced machine learning techniques, and assess the model’s performance across different demographic groups to ensure equitable risk prediction. Methods : Data from 13,136 children aged 6-59 months from the 2018 National Demographic Health Survey (NDHS) were analysed. Sixteen machine learning algorithms were evaluated based on their ability to predict childhood anaemia using a wide range of individual, community, and environmental factors. The Extra Trees (ET) classifier, demonstrating the highest predictive performance, was used to identify the top ten predictors of childhood anaemia. A fairness and demographic bias assessment framework was incorporated to evaluate the model’s performance across different regions, wealth index categories, ethnic groups, and gender. Results : The ET classifier outperformed all other algorithms, achieving an area under the curve (AUC) of 0.8319, accuracy of 0.7565, and a recall of 0.7565. The top ten predictors identified by the ET model included the number of under-five children in the household, birth order, child age, media access, maternal health-seeking behaviour, child gender, proximity to water, money problems, day land surface temperature, and all population count. The demographic bias assessment revealed variations in model performance across different subgroups, with the lowest AUCs observed in the North-East region (0.79), the poorest wealth index category (0.80), and the Hausa/Fulani ethnic group (0.81). Conclusion : This study demonstrates the potential of machine learning techniques to accurately predict childhood anaemia in Nigeria and identify key risk factors that inform targeted interventions. Future research should focus on refining the predictive model, exploring integrated interventions, and deploying AI-based tools to combat childhood anaemia in Nigeria and beyond. Supplementary Material File (ml_manuscript_updated.docx) - Download - 969.64 KB Information & Authors Information Version history Peer review timeline Published Public Health Challenges Version of Record29 Sep 2025Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection

Authors Metrics & Citations Metrics Article Usage 300views 161downloads Citations Download citation Ibrahim Khalil Ja'afar, Olalekan A. Uthman. Predicting Childhood Anaemia in Nigeria: A Machine Learning Approach to Uncover Key Risk Factors. Authorea. 18 January 2025. DOI: https://doi.org/10.22541/au.173720390.08343435/v1 DOI: https://doi.org/10.22541/au.173720390.08343435/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.