Abstract
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.