Unveiling predictive factors for household-level stunting in India: A machine learning approach using NFHS-5 and satellite-driven data.

Abstract

Objectives: Childhood stunting remains a significant public health issue in India, affecting approximately 35% of children under 5. Despite extensive research, existing prediction models often fail to incorporate diverse data sources and address the complex interplay of socioeconomic, demographic, and environmental factors. This study bridges this gap by employing machine learning methods to predict stunting at the household level, using data from the National Family Health Survey combined with satellite-driven datasets.

Methods: We used four machine learning models-random forest regression, support vector machine regression, K-nearest neighbors regression, and regularized linear regression-to examine the impact of various factors on stunting. The random forest regression model demonstrated the highest predictive accuracy and robustness.

Results: The proportion of households below the poverty line and the dependency ratio consistently predicted stunting across all models, underscoring the importance of economic status and household structure. Moreover, the educational level of the household head and environmental variables such as average temperature and leaf area index were significant contributors. Spatial analysis revealed significant geographic clustering of high-stunting districts, notably in central and eastern India, further emphasizing the role of regional socioeconomic and environmental factors. Notably, environmental variables like average temperature and leaf area index emerged as strong predictors of stunting, highlighting how regional climate and vegetation conditions shape nutritional outcomes.

Conclusions: These findings underline the importance of comprehensive interventions that not only address socioeconomic inequities but also consider environmental factors, such as climate and vegetation, to effectively combat childhood stunting in India.