Learning Bayesian networks from demographic and health survey data.
Child mortality from preventable diseases such as pneumonia and diarrhoea in low and middle-income countries remains a serious global challenge. We combine knowledge with available Demographic and Health Survey (DHS) data from India, to construct Causal Bayesian Networks (CBNs) and investigate the f...
| Published in: | Journal of Biomedical Informatics Vol. 113 |
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| Main Authors: | , |
| Format: | research Journal Article |
| Published: |
Academic Press Inc.
Jan2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148234626&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148234626 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Jan2021 vid: 113 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 148234626 148234626 NLM33217542 148234626 10.1016/j.jbi.2020.103588 NLM33217542 148234626 ppct: 1 formats: tig: atl: Learning Bayesian networks from demographic and health survey data. aug: au: Kitson, Neville Kenneth Constantinou, Anthony C. affil: Bayesian Artificial Intelligence Research Lab, Risk and Information Management (RIM) Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London (QMUL), London E1 4NS, UK sug: subj: Knowledge Bases Algorithms Human Probability Demography Sample Size Child Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Child: 6-12 years ab: Child mortality from preventable diseases such as pneumonia and diarrhoea in low and middle-income countries remains a serious global challenge. We combine knowledge with available Demographic and Health Survey (DHS) data from India, to construct Causal Bayesian Networks (CBNs) and investigate the factors associated with childhood diarrhoea. We make use of freeware tools to learn the graphical structure of the DHS data with score-based, constraint-based, and hybrid structure learning algorithms. We investigate the effect of missing values, sample size, and knowledge-based constraints on each of the structure learning algorithms and assess their accuracy with multiple scoring functions. Weaknesses in the survey methodology and data available, as well as the variability in the CBNs generated by the different algorithms, mean that it is not possible to learn a definitive CBN from data. However, knowledge-based constraints are found to be useful in reducing the variation in the graphs produced by the different algorithms, and produce graphs which are more reflective of the likely influential relationships in the data. Furthermore, valuable insights are gained into the performance and characteristics of the structure learning algorithms. Two score-based algorithms in particular, TABU and FGES, demonstrate many desirable qualities; (a) with sufficient data, they produce a graph which is similar to the reference graph, (b) they are relatively insensitive to missing values, and (c) behave well with knowledge-based constraints. The results provide a basis for further investigation of the DHS data and for a deeper understanding of the behaviour of the structure learning algorithms when applied to real-world settings. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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