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dc.contributor.authorAdegbosin, Adeyinka
dc.contributor.authorStantic, Bela
dc.contributor.authorSun, Jing
dc.date.accessioned2020-08-18T05:29:56Z
dc.date.available2020-08-18T05:29:56Z
dc.date.issued2020
dc.identifier.issn2044-6055
dc.identifier.doi10.1136/bmjopen-2019-034524
dc.identifier.urihttp://hdl.handle.net/10072/396526
dc.description.abstractObjectives: 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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherBMJ Journals
dc.relation.ispartofpagefrome034524
dc.relation.ispartofissue8
dc.relation.ispartofjournalBMJ Open
dc.relation.ispartofvolume10
dc.subject.fieldofresearchPaediatrics
dc.subject.fieldofresearchBiomedical and clinical sciences
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchHealth services and systems
dc.subject.fieldofresearchPublic health
dc.subject.fieldofresearchOther health sciences
dc.subject.fieldofresearchHealth sciences
dc.subject.fieldofresearchPsychology
dc.subject.fieldofresearchcode3213
dc.subject.fieldofresearchcode32
dc.subject.fieldofresearchcode3202
dc.subject.fieldofresearchcode4203
dc.subject.fieldofresearchcode4206
dc.subject.fieldofresearchcode4299
dc.subject.fieldofresearchcode42
dc.subject.fieldofresearchcode52
dc.titleEfficacy of Deep Learning Methods for predicting Under-five mortality in 34 Low and Middle-Income Countries
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationAdegbosin, A; Stantic, B; Sun, J, Efficacy of Deep Learning Methods for predicting Under-five mortality in 34 Low and Middle-Income Countries, BMJ Open, 2020, 10 (8), pp. e034524
dcterms.licensehttp://creativecommons.org/licenses/by-nc/4.0/
dc.date.updated2020-07-15T10:43:49Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© Author(s) (or their employer(s)) 2020. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
gro.hasfulltextFull Text
gro.griffith.authorStantic, Bela


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