dc.contributor.author | Adegbosin, Adeyinka | |
dc.contributor.author | Stantic, Bela | |
dc.contributor.author | Sun, Jing | |
dc.date.accessioned | 2020-08-18T05:29:56Z | |
dc.date.available | 2020-08-18T05:29:56Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 2044-6055 | |
dc.identifier.doi | 10.1136/bmjopen-2019-034524 | |
dc.identifier.uri | http://hdl.handle.net/10072/396526 | |
dc.description.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. | |
dc.description.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | BMJ Journals | |
dc.relation.ispartofpagefrom | e034524 | |
dc.relation.ispartofissue | 8 | |
dc.relation.ispartofjournal | BMJ Open | |
dc.relation.ispartofvolume | 10 | |
dc.subject.fieldofresearch | Paediatrics | |
dc.subject.fieldofresearch | Biomedical and clinical sciences | |
dc.subject.fieldofresearch | Clinical sciences | |
dc.subject.fieldofresearch | Health services and systems | |
dc.subject.fieldofresearch | Public health | |
dc.subject.fieldofresearch | Other health sciences | |
dc.subject.fieldofresearch | Health sciences | |
dc.subject.fieldofresearch | Psychology | |
dc.subject.fieldofresearchcode | 3213 | |
dc.subject.fieldofresearchcode | 32 | |
dc.subject.fieldofresearchcode | 3202 | |
dc.subject.fieldofresearchcode | 4203 | |
dc.subject.fieldofresearchcode | 4206 | |
dc.subject.fieldofresearchcode | 4299 | |
dc.subject.fieldofresearchcode | 42 | |
dc.subject.fieldofresearchcode | 52 | |
dc.title | Efficacy of Deep Learning Methods for predicting Under-five mortality in 34 Low and Middle-Income Countries | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dcterms.bibliographicCitation | Adegbosin, 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.license | http://creativecommons.org/licenses/by-nc/4.0/ | |
dc.date.updated | 2020-07-15T10:43:49Z | |
dc.description.version | Version 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.hasfulltext | Full Text | |
gro.griffith.author | Stantic, Bela | |