Dataset Information

Efficacy of deep learning methods for predicting under-five mortality in 34 low-income and middle-income countries.

ABSTRACT:

Objectives

To explore the efficacy of machine learning (ML) techniques in predicting under-five mortality (U5M) in low-income and middle-income countries (LMICs) and to identify significant predictors of U5M.

Design

This is a cross-sectional, proof-of-concept study.

Settings and participants

We analysed data from the Demographic and Health Survey. The data were drawn from 34 LMICs, comprising a total of n=1 520 018 children drawn from 956 995 unique households.

Primary and secondary outcome measures

The primary outcome measure was U5M; secondary outcome was comparing the efficacy of deep learning algorithms: deep neural network (DNN); convolution neural network (CNN); hybrid CNN-DNN with logistic regression (LR) for the prediction of child's survival.