Machine Learning Based Detection and Classification of Child Malnutrition Using Anthropometric and Demographic Data
DOI:
https://doi.org/10.65761/jbs.v2.i2.19Keywords:
Malnutrition in children, Random Forest, Multiclass Classification, Data-driven healthcareAbstract
Background: Malnutrition among children remains a critical public health concern, particularly in regions with limited access to timely medical assessment.
Objective: This study presents a machine learning–based approach for the automated classification of child malnutrition using anthropometric and demographic data. The proposed system utilizes age, gender, height, and weight to predict nutritional status categories, including stunted, wasted, underweight, and overweight.
Methods: A publicly available malnutrition dataset was preprocessed through data cleaning, feature scaling, and class imbalance handling using oversampling techniques. Multiple supervised machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting, were trained and evaluated. Model performance was assessed using accuracy, precision, recall, F1-score, and confusion matrices.
Results: Experimental results indicate that ensemble-based models, particularly Random Forest, achieve superior classification performance.
Implication: : This work contributes to the growing body of evidence supporting AI-assisted pediatric health screening and offers a reproducible baseline for future research. The findings demonstrate the potential of machine learning as a decision-support tool for early malnutrition screening; however, the system is intended solely as an aid and not a substitute for professional medical diagnosis.
References
1. Dipasquale V, Cucinotta U, Romano C. Acute malnutrition in children: pathophysiology, clinical effects and treatment. Nutrients. 2020;12(8):2413.
2. World Health Organization. Malnutrition in children: consequences and implications [Internet]. Geneva: World Health Organization; [cited 2025 Jan]. Available from: https://www.who.int/data/nutrition/nlis/info/malnutrition-in-children
3. Becker PJ, Carney LN, Corkins MR, Monczka J, Smith E, Smith SE, et al. Consensus statement of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition: indicators recommended for the identification and documentation of pediatric malnutrition (undernutrition). J Acad Nutr Diet. 2014;114(12):1988-2000.
4. St-Jules DE, Lloyd L, Meade A, Biruete A, Kistler B, Carrero JJ. Deconstructing disease-related malnutrition: a new assessment framework for clinical practice. J Ren Nutr. 2023;33(6):707-16.
5. Talukder A, Ahammed B. Machine learning algorithms for predicting malnutrition among under-five children in Bangladesh. Nutrition. 2020;78:110861.
6. Jain S, Khanam T, Abedi AJ, Khan AA. Efficient machine learning for malnutrition prediction among under-five children in India. In: 2022 IEEE Delhi Section Conference (DELCON); 2022 Feb 11-13; New Delhi, India. Piscataway (NJ): IEEE; 2022. p. 1-10.
7. Kar S, Pratihar S, Nayak S, Bal S, HL G. Prediction of child malnutrition using machine learning. In: 2021 10th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON); 2021 Dec 2-4; Bhubaneswar, India. Piscataway (NJ): IEEE; 2021. p. 1-4.
8. Constenla-Villoslada S, Liu Y, McBride L, Ouma C, Mutanda N, Barrett CB. High-frequency monitoring enables machine learning-based forecasting of acute child malnutrition for early warning. Proc Natl Acad Sci U S A. 2025;122(23):e2416161122.
9. Janssen SM, Bouzembrak Y, Tekinerdogan B. Artificial intelligence in malnutrition: a systematic literature review. Adv Nutr. 2024;15(9):100264.
10. Rao B, Rashid M, Hasan MG, Thunga G. Machine learning in predicting child malnutrition: a meta-analysis of demographic and health surveys data. Int J Environ Res Public Health. 2025;22(3):449.
11. Usman M, Kopczewska K. Spatial and machine learning approach to model childhood stunting in Pakistan: role of socio-economic and environmental factors. Int J Environ Res Public Health. 2022;19(17):10967.
12. Biradar VG, Naik KK. Detection of malnutrition in children using deep learning model. In: International Conference on Smart Computing and Communication; 2024 Jan. Singapore: Springer Nature Singapore; 2024. p. 35-45.
13. Hendy A, Abdelaliem SMF, Sultan HM, Alahmedi SH, Ibrahim RK, Abdelrazek EME, et al. Unlocking insights: using machine learning to identify wasting and risk factors in Egyptian children under 5. Nutrition. 2025;131:112631.
14. Jeganathan M, Jeganathan SR. Predictions of malnutrition among children. In: Transforming healthcare. Boca Raton: CRC Press; 2025. p. 77-98.
15. Indrisari E, Febiansyah H, Adiwinoto B. A systematic literature review on the application of machine learning for predicting stunting prevalence in Indonesia (2020–2024). Jurnal Sisfokom (Sistem Informasi dan Komputer). 2025;14(3):277-83.
16. Saleem MU, Aslam MU, Khatir AG, Jiang Q. Exploring risk factors and predictive modeling of child malnutrition in Pakistan using machine learning. Am J Hum Biol. 2025;37(9):e70124.
17. Khan MNA, Yunus RM. A hybrid ensemble approach to accelerate the classification accuracy for predicting malnutrition among under-five children in sub-Saharan African countries. Nutrition. 2023;108:111947.
18. Tamanna T, Mahmud S, Salma N, Hossain MM, Karim MR. Identifying determinants of malnutrition in under-five children in Bangladesh: insights from the BDHS-2022 cross-sectional study. Sci Rep. 2025;15(1):14336.
19. Worku MG, Mohanty I, Mengesha Z, Niyonsenga T. Magnitudes of various forms of undernutrition among children from the composite index of anthropometric failure in Sub-Saharan Africa: a systematic review and meta-analysis. Nutrients. 2025;17(11):1818.
20. 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.
21. 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;200340.
22. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321-57.
23. Najwa AF, Gunawan PH. Enhancing multiclass classification of child nutritional status using KNN and random forest with SMOTE. In: 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA); 2024. Piscataway (NJ): IEEE; 2024. p. 483-8.
24. Gedefaw AE, Mengistu AK, Maru TC, Walle AD, Alemayehu MA, Yehuala TZ, et al. Predicting malnutrition in PLWHIV using machine learning in Gondar, Ethiopia. BMC Public Health. 2025;25(1):3177.
25. Hosmer DW Jr, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. Hoboken (NJ): John Wiley & Sons; 2013.
26. Costa VG, Pedreira CE. Recent advances in decision trees: an updated survey. Artif Intell Rev. 2023;56(5):4765-800.
27. Salman HA, Kalakech A, Steiti A. Random forest algorithm overview. Babylonian J Mach Learn. 2024:69-79.
28. Pisner DA, Schnyer DM. Support vector machine. In: Machine learning. Cambridge (MA): Academic Press; 2020. p. 101-21.
29. Ayodele A. A comparative study of ensemble learning techniques for imbalanced classification problems. World J Adv Res Rev. 2023;19(1):1633-43.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Faisal Hameed, Atif Masih, Ahsan Balal Shaker, Syed Waqas Najeeb
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
