DOI: 10.1101/19007583Sep 25, 2019Paper

Predicting Under-five mortality across 21 Low and Middle-Income Countries using Deep Learning Methods

MedRxiv : the Preprint Server for Health Sciences
A. E. AdegbosinJing Sun


Objectives: To explore the efficacy of Machine Learning (ML) techniques in predicting under-five mortality in LMICs and to identify significant predictors of under-five mortality (U5M). Design: This is a cross-sectional, proof-of-concept study. Settings and participants We analysed data from the Demographic and Health Survey (DHS). The data was drawn from 21 Low-and-Middle Income Countries (LMICs) countries (N = 1,048,575). Eligible mothers in each household were asked information about their children and the reproductive care they received during the pregnancy. Primary and secondary outcome measures: The primary outcome measure was under-five mortality; 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 survival. Results: We found that duration of breast feeding, household wealth index and the level of maternal education are the most important predictors of under-five mortality. We found that deep learning techniques are superior to LR for the classification of child survival: LR sensitivity = 0.47, specificity = 0.53; DNN sensitivity = 0.69, specificity = 0.83; CNN sensit...Continue Reading

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