Stunting Classification and Prediction in Children Under Five: A Systematic Review of Machine Learning-Based Studies

Fanny Kartika Fajriyani, Ropitasari Ropitasari, Riska Fajar Fatony, Nahdiyah Karimah

Abstract


Background: Childhood stunting remains a major public health problem, particularly in low- and middle-income countries. Machine learning may support the identification and prediction of stunting by integrating multidimensional information related to maternal, child, household, socioeconomic, environmental, and nutritional factors. This systematic review aimed to synthesize evidence on machine learning-based models for stunting classification and prediction among children under five, including predictor domains, algorithm performance, model interpretability, and potential applications in child health programs.

Method: A systematic review was conducted according to PRISMA 2020 guidelines. Studies published from January 2021 to January 2026 were searched in PubMed, Scopus, Google Scholar, Web of Science, and ScienceDirect. Eligible studies involved children under five, applied machine learning or deep learning algorithms to stunting-related outcomes, and reported at least one model-performance measure. Data were synthesized narratively, and methodological quality and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST).

Results: Fifteen studies met the inclusion criteria. Frequently reported predictor domains included maternal education, household socioeconomic status, birth weight, child age, maternal characteristics, sanitation, birth-related factors, and geographical conditions. Random Forest, Gradient Boosting, and Extreme Gradient Boosting were commonly evaluated and demonstrated competitive performance across different datasets. However, comparisons were limited by heterogeneity in populations, predictors, preprocessing procedures, outcome definitions, validation strategies, and performance metrics.

Conclusion: Machine learning-based models show potential for identifying and predicting childhood stunting. Further research should emphasize clear outcome definitions, external validation, transparent reporting, and evaluation in routine child health services before widespread implementation.

Keywords


stunting; classification; prediction; machine learning; children under five; systematic review

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References


  1. Benjamin-Chung J, Mertens A, Colford JM, Hubbard A, van der Laan MJ, Coyle J, et al. Early-childhood linear growth faltering in low- and middle-income countries. Nature. 2023;621:550–557. doi:10.1038/s41586-023-06418-5.
  2. Mehta S, Huey SL, Fahim SM, Sinha S, Rajagopalan K, Ahmed T, et al. Advances in artificial intelligence and precision nutrition approaches to improve maternal and child health in low-resource settings. Nat Commun. 2025;16:7673. doi:10.1038/s41467-025-62985-3.
  3. Robertson RC, Edens TJ, Carr L, Mutasa K, Gough E, Evans C, et al. The gut microbiome and early-life growth in a population with high prevalence of stunting. Nat Commun. 2023;14:654. doi:10.1038/s41467-023-36135-6.
  4. Mertens A, Benjamin-Chung J, Colford JM, Hubbard A, van der Laan MJ, Coyle J, et al. Child wasting and concurrent stunting in low- and middle-income countries. Nature. 2023;621:558–567. doi:10.1038/s41586-023-06480-z.
  5. Khan JR, Tomal JH, Raheem E. Model and variable selection using machine learning methods with applications to childhood stunting in Bangladesh. Inform Health Soc Care. 2021;46(4):425–442. doi:10.1080/17538157.2021.1904938.
  6. Food and Agriculture Organization of the United Nations, International Fund for Agricultural Development, United Nations Children’s Fund, World Food Programme, World Health Organization. The state of food security and nutrition in the world 2024. Rome: FAO; 2024. doi:10.4060/cd1254en.
  7. De Francesco D, Reiss JD, Roger J, Tang AS, Chang AL, Becker M, et al. Data-driven longitudinal characterization of neonatal health and morbidity. Sci Transl Med. 2023;15(683):eadc9854. doi:10.1126/scitranslmed.adc9854.
  8. Mertens A, Benjamin-Chung J, Colford JM, Coyle J, van der Laan MJ, Hubbard A, et al. Causes and consequences of child growth faltering in low-resource settings. Nature. 2023;621:568–576. doi:10.1038/s41586-023-06501-x.
  9. Keleş E, Bağcı U. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. NPJ Digit Med. 2023;6:220. doi:10.1038/s41746-023-00941-5.
  10. Banda C, Nyanjahia AP. Digital health information systems and the management of child stunting in Malawi: a practice-based intervention study from a social work perspective. Int J Innov Sci Res Technol. 2026;11(1). doi:10.38124/ijisrt/26jan663.
  11. Shen H, Zhao H, Jiang Y. Machine learning algorithms for predicting stunting among under-five children in Papua New Guinea. Children (Basel). 2023;10(10):1638. doi:10.3390/children10101638.
  12. Fitzgerald R, Manguerra H, Arndt MB, Gardner WM, Chang YY, Zigler B, et al. Current dichotomous metrics obscure trends in severe and extreme child growth failure. Sci Adv. 2022;8(20):eabm8954. doi:10.1126/sciadv.abm8954.
  13. Headey D, Ruel MT. Food inflation and child undernutrition in low- and middle-income countries. Nat Commun. 2023;14:5761. doi:10.1038/s41467-023-41543-9.
  14. Fontaine F, Turjeman S, Callens K, Koren O. The intersection of undernutrition, microbiome, and child development in the first years of life. Nat Commun. 2023;14:3554. doi:10.1038/s41467-023-39285-9.
  15. Matos YAS, Cano J, Shafiq H, Williams C, Sunny J, Cowardin CA. Colonization during a key developmental window reveals microbiota-dependent shifts in growth and immunity during undernutrition. Microbiome. 2024;12:71. doi:10.1186/s40168-024-01783-3.
  16. Javidi H, Mariam A, Khademi G, Zabor EC, Zhao R, Radivoyevitch T, et al. Identification of robust deep neural network models of longitudinal clinical measurements. NPJ Digit Med. 2022;5:106. doi:10.1038/s41746-022-00651-4.
  17. Nasarian E, Alizadehsani R, Acharya UR, Tsui K. Designing interpretable machine learning systems to enhance trust in healthcare: a systematic review and proposed responsible clinician-AI collaboration framework. Inf Fusion. 2024;108:102412. doi:10.1016/j.inffus.2024.102412.
  18. Dhanda SS, Panwar D, Lin CC, Sharma TK, Rastogi D, Bindewari S, et al. Advancement in public health through machine learning: a narrative review of opportunities and ethical considerations. J Big Data. 2025;12:154. doi:10.1186/s40537-025-01201-x.
  19. Ciecierski-Holmes T, Singh R, Axt M, Brenner S, Barteit S. Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review. NPJ Digit Med. 2022;5:162. doi:10.1038/s41746-022-00700-y.
  20. Cascarano A, Mur-Petit J, Hernández-González J, Camacho M. Machine and deep learning for longitudinal biomedical data: a review of methods and applications. Artif Intell Rev. 2023;56:1711–1771. doi:10.1007/s10462-023-10561-w.
  21. Deng W, O’Brien MK, Andersen RA, Rai R, Jones E, Jayaraman A. A systematic review of portable technologies for the early assessment of motor development in infants. NPJ Digit Med. 2025;8:63. doi:10.1038/s41746-025-01450-3.
  22. Novalina N, Tarigan IAA, Kameela FK, Rizkinia M. Benchmarking machine learning algorithm for stunting risk prediction in Indonesia. Bull Electr Eng Inform. 2025;14(3):2252–2263. doi:10.11591/eei.v14i3.8997.
  23. Muralidharan V, Burgart AM, Daneshjou R, Rose S. Recommendations for the use of pediatric data in artificial intelligence and machine learning: ACCEPT-AI. NPJ Digit Med. 2023;6:166. doi:10.1038/s41746-023-00898-5.
  24. Ndagijimana S, Kabano IH, Masabo E, Ntaganda JM. Prediction of stunting among under-5 children in Rwanda using machine learning techniques. J Prev Med Public Health. 2023;56(1):41–49. doi:10.3961/jpmph.22.388.
  25. Mkungudza J, Twabi HS, Manda SOM. Development of a diagnostic predictive model for determining child stunting in Malawi: a comparative analysis of variable selection approaches. BMC Med Res Methodol. 2024;24:175. doi:10.1186/s12874-024-02283-6.
  26. Hibberd ML, Webber DM, Rodionov DA, et al. Bioactive glycans in a microbiome-directed food for children with malnutrition. Nature. 2023;625:157–165. doi:10.1038/s41586-023-06838-3.
  27. Hendy A, Ibrahim RK, Abdelaliem SMF, Zaher A, Alkubati SA, Abd El-kader RG, et al. Supervised machine learning for classification and prediction of stunting among under-five Egyptian children. BMC Pediatr. 2025;25:681. doi:10.1186/s12887-025-06138-x.
  28. Mgomezulu WR, Thangata P, Mkandawire B, Amoah N. Advancing predictive analytics in child malnutrition: machine, ensemble and deep learning models with balanced class distribution for early detection of stunting and wasting. Hum Nutr Metab. 2025;42:200340. doi:10.1016/j.hnm.2025.200340.
  29. Carneiro P, Kraftman L, Mason G, Moore L, Rasul I, Scott M. The impacts of a multifaceted prenatal intervention on human capital accumulation in early life. Am Econ Rev. 2021;111:2506–2549. doi:10.1257/aer.20191726.
  30. United Nations Department of Economic and Social Affairs. The Sustainable Development Goals report 2024. New York: United Nations; 2024. doi:10.18356/9789213589755.
  31. Wicaksono A, Prasetyo D, Mar’atullatifah Y, Iswavigra DU, Mahmudah H, Hapsari A. Data analysis and explainable machine learning for stunting prediction. J Artif Intell Leg Technol. 2025;1(1):1–10.
  32. Anhar MF, Soewito B. Application of XGBoost-based machine learning methods to predict stunting. Int J Artif Intell Res. 2025;9(1.1). doi:10.29099/ijair.v9i1.1.1542.
  33. Mulyani H, Musawarman, Faturrochman R, Permana DH. Machine learning-based early detection of stunting and intervention recommendations. Bit-Tech. 2025;8(2):2160–2170. doi:10.32877/bt.v8i2.3213.
  34. Sutarmi, Warijan, Indrayana T, Putro DP, Gunawan I. Machine learning model for stunting prediction. J Health Sains. 2023;4(9):10–23.
  35. Wijeakumar S, Forbes SH, Magnotta VA, Deoni S, Jackson K. Stunting in infancy is associated with atypical activation of working memory and attention networks. Nat Hum Behav. 2023;7:2199–2211. doi:10.1038/s41562-023-01725-3.
  36. Sahamony NF, Terttiaavini, Rianto H. Analysis of performance comparison of machine learning models for predicting stunting risk in children’s growth. MALCOM Indones J Mach Learn Comput Sci. 2024;4(2):413–422. doi:10.57152/malcom.v4i2.1210.
  37. Anku EK, Duah HO. Predicting and identifying factors associated with undernutrition among children under five years in Ghana using machine learning algorithms. PLoS One. 2024;19(2):e0296625. doi:10.1371/journal.pone.0296625.
  38. Islam MM, Kibria NMSJ, Kumar S, Roy DC, Karim MR. Prediction of undernutrition and identification of its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms. PLoS One. 2024;19(12):e0315393. doi:10.1371/journal.pone.0315393.
  39. Ayele MK, Baye GA, Yesuf SH, Engda AA, Mitiku ET. Predicting stunting status among under-five children in Ethiopia using ensemble machine learning algorithms. Sci Rep. 2025;15:27907. doi:10.1038/s41598-025-03206-1.




DOI: https://doi.org/10.20961/placentum.v14i2.123751

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