Determinants of childhood diarrhea in low- and middle-income countries: a comparative analysis of epidemiological and machine learning approaches

The study stresses the significance of core modifiable determinants such as maternal education and contextual wealth. Methodological triangulation illustrates the complementarity of classic regression for inference and machine learning for feature discovery. These findings justify the imposition of hyper-localized, multi-sectoral interventions on high-risk age groups and areas based on sound data analysis to optimize public health resources. This mixed approach provides a scalable model for disease burden measurement in LMICs.