An Enhanced Machine Learning Approach for Early Detection ‎of Malnutrition in Toddlers, Using Health and Social‎Determinants Data

  • Authors

    • Dr.J. Karunanithi Associate Professor & Research Head, Dept. of computer science, TSA AST College Perur,Coimbatore-10
    • E. Sundaravalli Research scholar, Dept. of computer science, TSA AST College Perur,Coimbatore-10
    https://doi.org/10.14419/9tm84p85

    Received date: May 28, 2025

    Accepted date: June 20, 2025

    Published date: July 8, 2025

  • Enhanced; Machine; Malnutrition; Data; Health

  • Abstract

    National progress and its global standing start with the health of the early-age population. The well-nourished children have become skilled, ‎constructive, and efficient resources to make innovative establishments. This will lead their country to be stronger and more successful in the ‎global economy and its overall well-being. Many countries like India, Japan, the US, Germany, South Korea, and Switzerland started to ‎invest in children’s health care and early education systems. The Indian government has implemented several schemes and plans to ‎prioritize child health care to be more competitive globally. Still, child malnutrition remains one of the challenging issues due to its vast ‎population and cultural differences. These challenges will be rectified by monitoring the child's nutrition status with powerful actions and ‎plans. The machine learning models were helpful in various sectors of decision-making mechanisms. This paper implemented the enhanced ‎machine learning model to detect the malnutrition status of children up to 5 years old. The malnutrition status can be measured by several ‎indicators. This model takes diet routine, socio-economic status, family type, health check-up routines, vaccination routines, exclusive ‎breastfeeding, demographic data such as gender, age, and anthropometric measures such as height and weight to estimate the nutrition status ‎of the toddlers. The enhanced machine learning model combines the results of the Support Vector Machine, Logistic regression, Gaussian ‎Naïve Bayes, and Random Forest machine learning models to produce an output with 94% accuracy, demonstrating the problem using real-world instances‎.

  • References

    1. Ibrahim, R. M., Priyadarsini, S. P., Nayeem, R. A., Somasundaram, V. M., Kumar, N. S., & Balasubramanian, R. (2022). A cross-sectional study on the prevalence and clinico-social profile of high risk pregnancies in rural Tamil Nadu, India. Journal of Clinical and Diagnostic Research, 16(3), LC11–LC15. https://doi.org/10.7860/JCDR/2022/55133.16106.
    2. Vaidyanathan, G., V. R. M., T. S., & others. (2022). Innovations in primary healthcare: A review of initiatives to promote maternal health in Tamil Nadu. Journal of Health Management, 24(1), 22–30. https://doi.org/10.1177/09720634221078697.
    3. Ch, D., & Sravani, S. (2024). Performance Evaluation of Mesh, Spidergon, and Mesh of Spidergon (MoS) Topologies in Network on Chip (NoC) Architectures. Journal of VLSI Circuits and Systems, 6(2), 130-140. https://doi.org/10.31838/jvcs/06.02.15.
    4. Kadhum, A. N., & Kadhum, A. N. (2024). Comparison Between the Yolov4 and Yolov5 Models in Detecting Faces while Wearing a Mask. International Academic Journal of Science and Engineering, 11(1), 01–08. https://doi.org/10.9756/IAJSE/V11I1/IAJSE1101.
    5. Vijayachandrika, C. (2022). The empowerment of women in Tamil Nadu: A multi-dimensional approach. Journal of Women Empowerment and Studies (JWES), 2(6), 1–7. https://doi.org/10.55529/jwes.26.1.7.
    6. Countdown to 2030 Collaboration. (2018). Countdown to 2030: Tracking progress towards universal coverage for reproductive, maternal, newborn, and child health. The Lancet, 391(10129), 1538–1548. https://doi.org/10.1016/S0140-6736(18)30104-1.
    7. Mohseni, M., & Aryankhesal, A. (2020). Developing a model for prevention of malnutrition among children under 5 years old. BMC Health Ser-vices Research, 20, 718. https://doi.org/10.1186/s12913-020-05567-x.
    8. Galler, J. R., Bringas-Vega, M. L., Tang, Q., Rabinowitz, A. G., Musa, K. I., Chai, W. J., Omar, H., Abdul Rahman, M. R., Abd Hamid, A. I., Ab-dullah, J. M., & Valdés-Sosa, P. A. (2021). Neurodevelopmental effects of childhood malnutrition: A neuroimaging perspective. NeuroImage, 231, 117828. https://doi.org/10.1016/j.neuroimage.2021.117828.
    9. Cf, O. D. D. S. "Transforming our world: the 2030 Agenda for Sustainable Development." United Nations: New York, NY, USA (2015).]
    10. Chopra, H., Paul, B., Virk, A., & others. (2023). Triple burden of malnutrition among children in India: Current scenario and the way forward. In-dian Journal of Pediatrics, 90(Suppl 1), 95–103. https://doi.org/10.1007/s12098-023-04739-x.
    11. Corkins, M. R. (2017). Why is diagnosing pediatric malnutrition important? Nutrition in Clinical Practice, 32(1), 15–18. https://doi.org/10.1177/0884533616678767.
    12. Casadei K, Kiel J. Anthropometric Measurement. In: StatPearls. StatPearls Publishing, Treasure Island (FL); 2023. PMID: 30726000.
    13. Iddrisu, I., Monteagudo-Mera, A., Poveda, C., Pyle, S., Shahzad, M., Andrews, S., & Walton, G. E. (2021). Malnutrition and gut microbiota in children. Nutrients, 13(8), 2727. https://doi.org/10.3390/nu13082727.
    14. Shrimpton, Roger. "Malnutrition." Oxford Research Encyclopedia of Global Public Health. 31 Mar. 2020; Accessed 20 Jul. 2024. https://doi.org/10.1093/acrefore/9780190632366.013.158.
    15. Larson-Nath, C., & Goday, P. (2019). Malnutrition in children with chronic disease. Nutrition in Clinical Practice, 34(3), 349–358. https://doi.org/10.1002/ncp.10274.
    16. Gragnolati M, Shekar M, Das Gupta M, Bredenkamp C, Lee YK. India’s Undernourished Children: A Call for Reform and Action. HNP Discus-sion Paper Series. Washington, DC: World Bank; 2005. https://doi.org/10.1596/13644.
    17. Castrogiovanni, P., & Imbesi, R. (2017). The role of malnutrition during pregnancy and its effects on brain and skeletal muscle postnatal develop-ment. Journal of Functional Morphology and Kinesiology, 2(3), 30. https://doi.org/10.3390/jfmk2030030.
    18. Richter, L. M., Daelmans, B., Lombardi, J., Heymann, J., Boo, F. L., Behrman, J. R., Lu, C., Lucas, J. E., Perez-Escamilla, R., Dua, T., Bhutta, Z. A., Stenberg, K., Gertler, P., & Darmstadt, G. L.; Paper 3 Working Group and the Lancet Early Childhood Development Series Steering Commit-tee. (2017). Investing in the foundation of sustainable development: Pathways to scale up for early childhood development. The Lancet, 389(10064), 103–118. https://doi.org/10.1016/S0140-6736(16)31698-1.
    19. Black, M., Lutter, C., & Trude, A. (2020). All children surviving and thriving: Re-envisioning UNICEF's conceptual framework of malnutrition. The Lancet Global Health, 8(7), e766–e767. https://doi.org/10.1016/S2214-109X(20)30122-4.
    20. Kar, S., Pratihar, S., Nayak, S., Bal, S., H. L., G., & V., R. (2021). Prediction of child malnutrition using machine learning. In 2021 10th Interna-tional Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON) (pp. 1–4). IEEE. https://doi.org/10.1109/IEMECON53809.2021.9689083.
    21. Browne, C., Matteson, D. S., McBride, L., Hu, L., Liu, Y., Sun, Y., et al. (2021). Multivariate random forest prediction of poverty and malnutrition prevalence. PLoS ONE, 16(9), e0255519. https://doi.org/10.1371/journal.pone.0255519.
    22. Larburu, N., Artola, G., Kerexeta, J., Caballero, M., Ollo, B., & Lando, C. M. (2022). Key factors and AI-based risk prediction of malnutrition in hospitalized older women. Geriatrics, 7(5), 105. https://doi.org/10.3390/geriatrics7050105.
    23. Islam, M. M., Rahman, M. J., Islam, M. M., Roy, D. C., Ahmed, N. F., Hussain, S., Amanullah, M., Abedin, M. M., & Maniruzzaman, M. (2022). Application of machine learning-based algorithm for prediction of malnutrition among women in Bangladesh. International Journal of Cognitive Computing in Engineering, 3, 46–57. https://doi.org/10.1016/j.ijcce.2022.02.002.
    24. Joseph, R., Sawant, V., Shenai, S., Paryani, M., & Patil, G. (2022). Machine learning-based factors affecting malnutrition and anemia among chil-dren in India. In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1826–1833). IEEE. https://doi.org/10.1109/ICICCS53718.2022.9788386.
    25. Kar, Shubham, Susmita Pratihar, Subhadip Nayak, Sauvik Bal, and Gururai HL. "Prediction of child malnutrition using machine learning." In 2021 10th international conference on internet of everything, microwave engineering, communication and networks (IEMECON), pp. 01-04. IEEE, 2021. https://doi.org/10.1109/IEMECON53809.2021.9689083.
    26. Talukder, Ashis, and Benojir Ahammed. "Machine learning algorithms for predicting malnutrition among under-five children in Bangla-desh." Nutrition 78 (2020): 110861. https://doi.org/10.1016/j.nut.2020.110861.
    27. Rao, B., Rashid, M., Hasan, M. G., & Thunga, G. (2025). Machine learning in predicting child malnutrition: A meta-analysis of demographic and health surveys data. International Journal of Environmental Research and Public Health, 22(3), 449. https://doi.org/10.3390/ijerph22030449.
    28. Kishore, K. Krishna, Jami Venkata Suman, I. Lakshmi Mnikyamba, Subba Rao Polamuri, and B. Venkatesh. "Prediction of malnutrition in new-bornInfants using machine learning techniques." (2023). https://doi.org/10.21203/rs.3.rs-2958834/v1.
    29. Joseph, R., Sawant, V., Shenai, S., Paryani, M., & Patil, G. (2022). Machine learning-based factors affecting malnutrition and anemia among chil-dren in India. In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1826–1833). IEEE. https://doi.org/10.1109/ICICCS53718.2022.9788386.
    30. Reusken, M., Coffey, C., Cruijssen, F., Melenberg, B., & van Wanrooij, C. (2025). 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(1). https://doi.org/10.1136/bmjph-2024-001460.
    31. Ramesh, S., Acharya, R., Sarna, A., Ashraf, S., Agrawal, P. K., Porwal, A., Khan, N., Deb, S., & Johnston, R. (2021). Using underweight for pre-dicting stunting among children in India: Analysis from the Comprehensive National Nutrition Survey. https://doi.org/10.21203/rs.3.rs-378194/v1.
    32. Zumma, T., Rahaman, A., Prova, N. N. I., Haque, T., Bose, J. C. J., & Youki, R. A. (2025). Early detection of childhood malnutrition using survey data and machine learning approaches. In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL) (pp. 833–838). IEEE. https://doi.org/10.1109/ICSADL65848.2025.10933198.
    33. El Haj, A., & Nazari, A. (2025). Optimizing renewable energy integration for power grid challenges to navigating. Innovative Reviews in Engineer-ing and Science, 3(2), 23–34.
    34. Wiśniewski, K. P., Zielińska, K., & Malinowski, W. (2025). Energy efficient algorithms for real-time data processing in reconfigurable computing environments. SCCTS Transactions on Reconfigurable Computing, 2(3), 1–7.
    35. Tsai, X., & Jing, L. (2025). Hardware-based security for embedded systems: Protection against modern threats. Journal of Integrated VLSI, Em-bedded and Computing Technologies, 2(2), 9–17.
    36. Surendar, A. (2025). Coordinated Control of Bidirectional EV Chargers for Grid Stability in High-Density Urban Networks. National Journal of Intelligent Power Systems and Technology, 1(1), 1-11.

  • Downloads

  • How to Cite

    Karunanithi, D., & Sundaravalli, E. . . (2025). An Enhanced Machine Learning Approach for Early Detection ‎of Malnutrition in Toddlers, Using Health and Social‎Determinants Data. International Journal of Basic and Applied Sciences, 14(SI-1), 270-278. https://doi.org/10.14419/9tm84p85