Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146411
Title: Predictors of contemporary under-5 child mortality in low- and middle-income countries : a machine learning approach
Authors: Bizzego, Andrea
Gabrieli, Giulio
Bornstein, Marc H.
Deater-Deckard, Kirby
Lansford, Jennifer E.
Bradley, Robert H.
Costa, Megan
Esposito, Gianluca
Keywords: Science::Medicine
Issue Date: 2021
Source: Bizzego, A., Gabrieli, G., Bornstein, M. H., Deater-Deckard, K., Lansford, J. E., Bradley, R. H., . . . Esposito, G. (2021). Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach. International Journal of Environmental Research and Public Health, 18(3), 1315-. doi:10.3390/ijerph18031315
Project: RG149/16
RT10/19
Journal: International journal of environmental research and public health
Abstract: Child Mortality (CM) is a worldwide concern, annually affecting as many as 6.81% children in low- and middle-income countries (LMIC). We used data of the Multiple Indicators Cluster Survey (MICS) (N = 275,160) from 27 LMIC and a machine-learning approach to rank 37 distal causes of CM and identify the top 10 causes in terms of predictive potency. Based on the top 10 causes, we identified households with improved conditions. We retrospectively validated the results by investigating the association between variations of CM and variations of the percentage of households with improved conditions at country-level, between the 2005-2007 and the 2013-2017 administrations of the MICS. A unique contribution of our approach is to identify lesser-known distal causes which likely account for better-known proximal causes: notably, the identified distal causes and preventable and treatable through social, educational, and physical interventions. We demonstrate how machine learning can be used to obtain operational information from big dataset to guide interventions and policy makers.
URI: https://hdl.handle.net/10356/146411
ISSN: 1660-4601
DOI: 10.3390/ijerph18031315
Rights: © 2021 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

Files in This Item:
File Description SizeFormat 
ijerph-18-01315-v2.pdf965.15 kBAdobe PDFView/Open

Page view(s)

21
Updated on Apr 9, 2021

Download(s)

1
Updated on Apr 9, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.