Predictors of Contemporary Under-5 Child Mortality in Low- and Middle-Income Countries: A Machine-Learning Approach

Abstract

Using a novel machine learning approach, we identify and prioritize in terms of predictive potency the top 10 distal causes of under-5 child mortality from almost 40 potential distal causes in more than a quarter of a million households in more than 25 low- and middle-income countries. Notably, all 10 distal causes are preventable and treatable through social, educational, and physical intervention. Thus, a unique contribution of our machine learning approach is to identify lesser-known preventable and treatable distal causes of under-5 child mortality that likely account for better-known proximal causes. Funding Statement: A.B. was supported by a Post-doctoral Fellowship within MIUR programme framework ”Dipartimenti di Eccellenza” (DiPSCO, University of Trento). G.E. was supported by NAP SUG 2015, Singapore Ministry of Education ACR Tier 1 (RG149/16 and RT10/19). M.H.B. was supported by the Intramural Research Program of the NIH/NICHD, USA, and an International Research Fellowship at the Institute for Fiscal Studies (IFS), London, UK, funded by the European Research Council (ERC) under the Horizon 2020 research and innovation programme (grant agreement No 695300- HKADeC-ERC-2015-AdG). Computational resources were provided by the National Super Computing Center of Singapore (Project ID: 12001609; Computational study of Child Development in Low Resource Contexts). Declaration of Interests: The Authors declare no conflict of interest. Ethics Approval Statement: Ethics approvals were handled in each site in which data were collected.