Understanding the social determinants of child mortality in Latin America over the last two decades: a machine learning approach.

ABSTRACT: The reduction of child mortality rates remains a significant global public health challenge, particularly in regions with high levels of inequality such as Latin America. We used machine learning (ML) algorithms to explore the relationship between social determinants and child under-5 mortality rates (U5MR) in Brazil, Ecuador, and Mexico over two decades. We created a municipal-level cohort from 2000 to 2019 and trained a random forest model (RF) to estimate the relative importance of social determinants in predicting U5MR. We conducted a sensitivity analysis training two more ML models and presenting the mean square error, root mean square error, and median absolute deviation. Our findings indicate that poverty, illiteracy, and the Gini index were the most important variables for predicti

SUBMITTER: Chivardi C 

PROVIDER: S-EPMC10682478 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature