An Explainable Stacking Ensemble Model for Predicting Childhood Malnutrition among Under-Five Children in Nigeria

Childhood malnutrition remains a major public health challenge in Nigeria, contributing to child morbidity, mortality, impaired cognitive development, and poor long-term socioeconomic outcomes. Although machine learning has shown promise for malnutrition prediction, many models operate as black boxes, limiting their interpretability and adoption in clinical and public health decision-making. This study developed an explainable stacking ensemble model for classifying and predicting childhood malnutrition among children Under-five in Nigeria, using the 2023–24 Nigeria Demographic and Health Survey (NDHS). Data from 9,374 children with valid anthropometric measurements were analysed, with malnutrition status determined using WHO Child Growth Standards based on height-for-age, weight-for-age, and weight-for-height z-scores. Following preprocessing and a multi-method, multi-phenotype feature selection process combining Recursive Feature Elimination, Mutual Information, and Random Forest Feature Importance, twenty-two socioeconomic, demographic, geographic, maternal, and child-health variables were retained for modelling. A stacking ensemble comprising Random Forest, XGBoost, LightGBM, CatBoost, and Support Vector Machine as base learners, with Logistic Regression as meta-learner, was developed using mother-level stratified group cross-validation to prevent clustering-related data leakage. Within-fold SMOTE addressed class imbalance, and Youden's J statistic guided threshold optimisation. Model interpretability was achieved using SHAP, providing both global and individual-level explanations. The stacking ensemble outperformed all individual base learners, achieving a ROC-AUC of 0.750, F1-score of 0.662, recall of 72.6%, precision of 61.0%, and accuracy of 69.9% at the optimal threshold of 0.40. SHAP analysis identified household wealth index, maternal education, child age, and geographic location as the most influential predictors of malnutrition risk. These findings demonstrate that integrating ensemble learning with explainable artificial intelligence provides an accurate, transparent, and practical decision-support framework for early identification of at-risk children, supporting evidence-based nutrition interventions and public health policy in Nigeria.

Childhood malnutrition, Stacking ensemble, Explainable artificial intelligence, SHAP

1. Akombi, B. J., Agho, K. E., Merom, D., Hall, J. J., & Renzaho, A. M. (2017). Multilevel analysis of factors associated with wasting and underweight among children under-five years in Nigeria. Nutrients, 9(1), 44. https://doi.org/10.3390/nu9010044 [Google Scholar] [Crossref]

2. Aryuni, M., Kumbangsila, M., Zakiyyah, A. Y., Sano, A. V. D., & Bhatti, F. M. (2023). Comparison of nutritional status prediction models of children under 5 years of age using supervised machine learning. Proceedings of the 3rd International Conference on Electronics, Biomedical Engineering, and Health Informatics, 1–10. https://doi.org/10.1007/978-981-99-0248-4_19 [Google Scholar] [Crossref]

3. Biau, G., & Scornet, E. (2016). A random forest guided tour. HAL. https://hal.science/hal-01307105 [Google Scholar] [Crossref]

4. Breiman, L., & Cutler, A. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 [Google Scholar] [Crossref]

5. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785 [Google Scholar] [Crossref]

6. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018 [Google Scholar] [Crossref]

7. Federal Ministry of Health and Social Welfare of Nigeria (FMoHSW), National Population Commission (NPC) [Nigeria], and ICF. (2025). Nigeria Demographic and Health Survey 2024. FMoHSW, NPC, and ICF. [Google Scholar] [Crossref]

8. Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1–3), 389–422. https://doi.org/10.1023/A:1012487302797 [Google Scholar] [Crossref]

9. Ijaiya, M. A., Anjorin, S., & Uthman, O. A. (2022). Individual and contextual factors associated with childhood malnutrition: A multilevel analysis of the double burden of childhood malnutrition in 27 countries. Journal of Global Health Science. https://doi.org/10.1186/s41256-022-00276-w [Google Scholar] [Crossref]

10. Islam, M. M., Kibria, N. M. S. J., Kumar, S., Roy, D. C., & Karim, M. R. (2024). Prediction of undernutrition and identification of its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms. PLOS ONE, 19(12), e0315393. https://doi.org/10.1371/journal.pone.0315393 [Google Scholar] [Crossref]

11. Jebero, Z., Moga, F., Gebremichael, B., & Tesfaye, T. (2023). Determinants of acute malnutrition among under-five children in governmental health facilities in Sodo Town, Southern Ethiopia: Unmatched case-control study. International Journal of Pediatrics. https://doi.org/10.1155/2023/3882801 [Google Scholar] [Crossref]

12. Kawo, M. A., Idris, M. N., Haruna, K., & Kaita, F. U. (2024). Predicting undernutrition risk factors using machine learning techniques in Nigerian under-five children. International Journal of Computer Science and Mobile Computing, 13(7), 56–70. https://doi.org/10.47760/ijcsmc.2024.v13i07.006 [Google Scholar] [Crossref]

13. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154. [Google Scholar] [Crossref]

14. Kishore, K. K., Suman, J. V., Mnikyamba, L. I., Polamuri, S. R., & Venkatesh, B. (2023). Prediction of malnutrition in newborn infants using machine learning techniques. Research Square. https://doi.org/10.21203/rs.3.rs-2958834/v1 [Google Scholar] [Crossref]

15. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. [Google Scholar] [Crossref]

16. Mahmud, T., Wara, T. U., & Joy, C. D. (2024). Risk factor identification and classification of malnutrition among under-five children in Bangladesh: Machine learning and statistical approach. arXiv. https://arxiv.org/abs/2412.05813 [Google Scholar] [Crossref]

17. Nirmani, H. M. C., & Kudagamage, U. P. (2024). Ensemble approach for early prediction of malnutrition level of children: A case study on children Under-five years old. Proceedings of the 2024 Moratuwa Engineering Research Conference (MERCon). [Google Scholar] [Crossref]

18. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 31, 6639–6649. [Google Scholar] [Crossref]

19. Qasrawi, R., Sgahir, S., Nemer, M., Halaikah, M., Badrasawi, M., Amro, M., Vicuna Polo, S., AbuAl-Halawa, D., Mujahed, D., & Nasreddine, L. (2024). Machine learning approach for predicting the impact of food insecurity on nutrient consumption and malnutrition in children aged 6 months to 5 years. Children, 11(7), 810. https://doi.org/10.3390/children11070810 [Google Scholar] [Crossref]

20. Reusken, M., Coffey, C., Cruijssen, F., et al. (2024). Identification of factors associated with acute malnutrition in children under 5 years and forecasting future prevalence: Assessing the potential of statistical and machine learning methods. BMJ Public Health, 3, e001460. https://doi.org/10.1136/bmjph-2024-001460 [Google Scholar] [Crossref]

21. Setegn, G. M., & Dejene, B. E. (2025). Improving machine learning models through explainable AI for predicting the level of dietary diversity among Ethiopian preschool children. Italian Journal of Pediatrics, 51(1), 45. https://doi.org/10.1186/s13052-025-01345-9 [Google Scholar] [Crossref]

22. Shahid, M., Yahya, M. A., Song, J., Naveed, H. M., Yuksel, S., Dincer, H., & Ali, M. (2025). Machine learning vs. traditional logistic regression: Predictive performance and risk factor identification for child nutritional outcome in Pakistan. BMC Public Health. https://doi.org/10.1186/s12889-025-25621-9 [Google Scholar] [Crossref]

23. Tamanna, T., Mahmud, S., Salma, N., Hossain, M. M., & Karim, M. R. (2025). Identifying determinants of malnutrition in under-five children in Bangladesh: Insights from the BDHS-2022 cross-sectional study. Scientific Reports, 15, 12345. https://doi.org/10.1038/s41598-025-99288-y [Google Scholar] [Crossref]

24. Tesfaw, L. M., & Fenta, H. M. (2021). Multivariate logistic regression analysis on the association between anthropometric indicators of under-five children in Nigeria: NDHS 2018. BMC Pediatrics, 21, Article 194. https://doi.org/10.1186/s12887-021-02657-5 [Google Scholar] [Crossref]

25. Tzitiridou-Chatzopoulou, M., Zournatzidou, G., Orovou, E., Lithoxopoulou, M., Drogouti, E., Sklavos, G., Antoniou, E., & Tsakalidis, C. (2024). Evaluating malnutrition practices and mother's education on children failure-to-thrive symptoms using entropy-weight and TOPSIS method. Children, 11(8), 903. https://doi.org/10.3390/children11080903 [Google Scholar] [Crossref]

26. UNICEF, WHO, & World Bank Group. (2023). Levels and trends in child malnutrition: Key findings of the 2023 edition. World Health Organization. https://www.who.int/ [Google Scholar] [Crossref]

27. Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural Computing and Applications, 24(1), 175–186. https://doi.org/10.1007/s00521-013-1368-0 [Google Scholar] [Crossref]

28. Wajgi, R., & Wajgi, D. (2022). Malnutrition detection in infants using machine learning approach. AIP Conference Proceedings. https://doi.org/10.1063/5.0076876 [Google Scholar] [Crossref]

29. World Health Organization. (2006). WHO child growth standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development. WHO. [Google Scholar] [Crossref]

30. World Health Organization. (2023). Levels and trends in child malnutrition. https://www.who.int/ [Google Scholar] [Crossref]

31. Yanto, M., Hadi, F., & Arlis, S. (2024). Determination of children's nutritional status with machine learning classification analysis approach. Indonesian Journal of Electrical Engineering and Computer Science, 33(1), 303–313. https://doi.org/10.11591/ijeecs.v33.i1.pp303-313 [Google Scholar] [Crossref]

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