BRIEF RESEARCH REPORT article
Front. Public Health
Sec. Public Health and Nutrition
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1663373
This article is part of the Research TopicRevolutionizing Nutritional Epidemiology: Harnessing Digital Health, AI, and Big Data for Population-Level Disease Prevention and ManagementView all 3 articles
RISE: A Novel Unified Framework for Feature Relevance in Malnutrition Analytics Integrating Statistical and Expert Insights
Provisionally accepted
- 1Department Of Computer Science, School of Arts and Sciences, Amrita Vishwa Vidyapeetham University, Mysuru, India
- 2Mysore Medical College and Research Institute, Mysuru, India
- 3Adelphi University,, New York, United States
- 4Mahatma Gandhi Medical College & RI, Pondicherry, India
- 5University of Mysore, Mysuru, India
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Addressing child malnutrition remains a critical global health priority, directly contributing to Sustainable Development Goals (SDG 2 – Zero Hunger and SDG 3 – Good Health and Well-being). This study aims to identify and prioritize the most influential determinants of acute forms of malnutrition among children aged 0–23 months by developing a novel feature scoring framework, RISE (Relevance-based Integration of Statistics and Expertise). The objective is to bridge the gap between data-driven modeling and context-specific insights by integrating model-based scores (from XGBoost), statistical filter methods for frequency boosting, and domain-informed adjustments. Using real-world data from Nutrition Rehabilitation Centre (NRC) at K.R. District Hospital, Mysuru, the RISE framework enhances the interpretability and contextual relevance of predictors often underweighted in traditional models. Domain-relevant features such as Mother Height, Breastfeeding Status, Caste, Maternal Working Status, and Ration card emerged as critical factors when adjusted through the RISE Framework. The top-ranked features included Child Weight, maternal anthropometry, and Child order remained consistently influential determinants, reflecting maternal dependency and the double burden of malnutrition. RISE uncovers hidden yet meaningful contributors that often go underrepresented in purely model-driven analyses. By adjusting feature scores to recognize both empirical strength and domain importance. By aligning analytical rigor with public health relevance, this study contributes a scalable, context-sensitive approach to feature prioritization in malnutrition research, supporting more informed, targeted interventions and policy actions toward achieving global nutrition goals.
Keywords: Child Malnutrition1, Maternal Malnutrition2, Feature Scoring3, Model-based Scoring4, Domain-based Scoring5, Filter-based Feature Scoring6, Frequency Boosting7, XGBoost8
Received: 10 Jul 2025; Accepted: 22 Sep 2025.
Copyright: © 2025 S, Govindarajan, S R, Antony, Uma and Rangarajan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Priya Govindarajan, priyagovindarajan@my.amrita.edu
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