This study proposes a data mining framework to identify factors contributing to child malnutrition in developing countries. It explores how data-driven insights can aid policy design and intervention planning. Using machine learning algorithms, the authors analyse demographic, socio-economic, and nutritional datasets to detect high-risk groups and actionable patterns. The approach supports evidence-based policy making to combat child malnutrition effectively.
Data Mining Approach to Minimise Child Malnutrition in Developing Countries
