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

Journal: BMJ open

PMID: 32801191

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.

Authors

Adeyinka Emmanuel Adegbosin

School of Medicine, Griffith University, Gold Coast, Queensland, Australia.
Bela Stantic

Institute for Integrated and Intelligent Systems, Griffith University, Queensland, Australia. Electronic address: b.stantic@griffith.edu.au.
Jing Sun

Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Keywords

Child Child Mortality Cross-Sectional Studies Deep Learning Developing Countries Female Humans Neural Networks, Computer Pregnancy

External Resources

View on PubMed Access via DOI PubMed (32801191)

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

Abstract

Authors

Keywords

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