Efficacy of Deep Learning Methods for predicting Under-five mortality in 34 Low and Middle-Income Countries

Abstract

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 34 Low-and-Middle Income Countries (LMICs) countries, comprising of a total of (N = 1,520,018 children drawn from 956,995 unique households. 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, number of antenatal visits, household wealth index, postnatal care and the level of maternal education are some of 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 sensitivity = 0.68, specificity = 0.83; CNN-DNN sensitivity = 0.71, specificity = 0.83. Conclusion: Our findings provide an understanding of determinants of U5M in LMICs. It also demonstrates that deep learning models are more efficacious than traditional analytical approach.