ArticleBMJ open2020
Efficacy of deep learning methods for predicting under-five mortality in 34 low-income and middle-income countries.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A machine learning approach to identify key socio-economic and demographic factors associated with neonatal mortality in Ethiopia.BMC public health · 2026Article
- Exploring the correlates of COVID-19 vaccination inequity: a global analysis using machine learning from a health economic lens.Frontiers in health services · 2026Article
- Understanding drivers of neonatal mortality in Zimbabwe: A machine learning approach using survey data.PLOS global public health · 2026Article
- Identifying determinants of under-5 mortality in Bangladesh: A machine learning approach with BDHS 2022 data.PloS one · 2025Article
- Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques.PloS one · 2025Article
- Applied artificial intelligence for global child health: Addressing biases and barriers.PLOS digital health · 2024Review
- Predicting the HIV/AIDS Knowledge among the Adolescent and Young Adult Population in Peru: Application of Quasi-Binomial Logistic Regression and Machine Learning Algorithms.International journal of environmental research and public health · 2023Article
- Data harnessing to nurture the human mind for a tailored approach to the child.Pediatric research · 2023Review
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- Machine Learning Algorithms for understanding the determinants of under-five Mortality.BioData mining · 2022Article
- A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence.International journal of environmental research and public health · 2022Review
- Trends of under-five mortality and associated risk factors in Zambia: a multi survey analysis between 2007 and 2018.BMC pediatrics · 2022Article
- Application of machine learning methods for predicting infant mortality in Rwanda: analysis of Rwanda demographic health survey 2014-15 dataset.BMC pregnancy and childbirth · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo 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.
designThis is a cross-sectional, proof-of-concept study. SETTINGS AND
participantsWe 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.
resultsWe 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 U5M. 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.
conclusionOur findings provide an understanding of determinants of U5M in LMICs. It also demonstrates that deep learning models are more efficacious than traditional analytical approach.
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