Random Forest Algorithm Applications in Studying Children Under-five: Systematic Literature Review (SLR)

Anas Tawalbeh; Maroning Useng; Sahidan Abdulmana

Authors

Anas Tawalbeh Faculty of Science and Technology, Fatoni University, Pattani 94160, Thailand
Maroning Useng Faculty of Science and Technology, Fatoni University, Pattani 94160, Thailand
Sahidan Abdulmana Faculty of Science and Technology, Fatoni University, Pattani 94160, Thailand

Keywords:

Systematic Literature Review, Random Forest, Under-five children, Risk prediction, Model validation

Abstract

Effective monitoring of health and development in children under five is essential for timely detection, informed clinical decision-making, and cost-effective public health action. Recent advances in machine learning have expanded the potential of under-five monitoring systems, particularly for early screening, population-level risk prediction, and determinant-oriented surveillance. This paper presents a systematic literature review of Random Forest and closely related modelling practices in under-five monitoring, with attention to both opportunities and methodological constraints. The review identified 15 eligible studies, covering applications in respiratory infection monitoring, malaria risk prediction, nutrition and growth surveillance, and developmental assessment, drawing on national survey microdata, clinical records, and imaging or audio modalities. The review concludes by outlining implications for researchers, practitioners, and educators, and by highlighting priorities for stronger evidence—particularly external validation, transparent leakage safeguards, and evaluation aligned with clinical purpose.

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