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Machine Learning in Predicting Child Malnutrition: A Meta-Analysis of Demographic and Health Surveys Data

Author

Listed:
  • Bhagyajyothi Rao

    (Department of Applied Statistics & Data Science, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal 576104, India)

  • Muhammad Rashid

    (Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, India
    Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA)

  • Md Gulzarull Hasan

    (Department of Applied Statistics & Data Science, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal 576104, India)

  • Girish Thunga

    (Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, India
    Centre for Toxicovigilance and Drug Safety, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal 576104, India)

Abstract

Background: Childhood malnutrition remains a significant global public health concern. The Demographic and Health Surveys (DHS) program provides specific data on child health across numerous countries. This meta-analysis aims to comprehensively assess machine learning (ML) applications in DHS data to predict malnutrition in children. Methods: A comprehensive search of the peer-reviewed literature in PubMed, Embase, and Scopus databases was conducted in January 2024. Studies employing ML algorithms on DHS data to predict malnutrition in children under 5 years were included. Using PROBAST (Prediction model Risk Of Bias Assessment Tool), the quality of the listed studies was evaluated. To conduct meta-analyses, Review Manager 5.4 was used. Results: A total of 11 out of 789 studies were included in this review. The studies were published between 2019 and 2023, with the major contribution from Bangladesh ( n = 6, 55%). Of these, ten studies reported stunting, three reported wasting, and four reported underweight. A meta-analysis of ten studies reported a pooled accuracy of 68.92% (95% CI: 66.04, 71.80; I 2 = 100%) among ML models for predicting stunting in children. Three studies indicated a pooled accuracy of 84.39% (95% CI: 80.90, 87.87; I 2 = 100%) in predicting wasting. A meta-analysis of four studies indicated a pooled accuracy of 73.60% (95% CI: 70.01, 77.20; I 2 = 100%) for ML models predicting underweight status in children. Conclusions: This meta-analysis indicated that ML models were observed to have moderate to good performance metrics in predicting malnutrition using DHS data among children under five years.

Suggested Citation

  • Bhagyajyothi Rao & Muhammad Rashid & Md Gulzarull Hasan & Girish Thunga, 2025. "Machine Learning in Predicting Child Malnutrition: A Meta-Analysis of Demographic and Health Surveys Data," IJERPH, MDPI, vol. 22(3), pages 1-15, March.
    • Handle: RePEc:gam:jijerp:v:22:y:2025:i:3:p:449-:d:1614749
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    References listed on IDEAS

    as
    1. S M Jubaidur Rahman & N A M Faisal Ahmed & Md Menhazul Abedin & Benojir Ahammed & Mohammad Ali & Md Jahanur Rahman & Md Maniruzzaman, 2021. "Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-11, June.
    2. Henny Suzana Mediani, 2020. "Predictors of Stunting Among Children Under Five Year of Age in Indonesia: A Scoping Review," Global Journal of Health Science, Canadian Center of Science and Education, vol. 12(8), pages 1-83, July.
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