A case study on quality of sleep and health using Bayesian networks
The objectives of this study are to investigate the associations of the socio-demographic characteristics, living habits, social and interpersonal factors and behaviour-related risk factors with both the quality of sleep and state of health. The tool used is Bayesian networks (BNs), a special cas...
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| Format: | Article |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2012
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| Online Access: | http://journalarticle.ukm.my/5921/ http://journalarticle.ukm.my/5921/ |
| Summary: | The objectives of this study are to investigate the associations of the socio-demographic
characteristics, living habits, social and interpersonal factors and behaviour-related risk factors
with both the quality of sleep and state of health. The tool used is Bayesian networks (BNs),
a special case of probabilistic graphical models. The data utilised in this study is collected by
employing the sample survey technique through the online network. A total of 1316 sets of
data are collected with 20 variables of interest being studied. Our situation is whereby the BNs
structure is unknown with full observability. A structural learning is conducted on the data to
learn the correct network structure. There are several phases involved including implementation
of the learning algorithms, integration of prior knowledge through the whitelist argument and
arc setting to form directed acyclic graphs. The network scores computation is implemented
to estimate the fitting of the resulting network of each structural learning algorithm in order to
choose the best-fitted network. The arc strength or edge intensity is computed to estimate the
marginal confidence on the presence of an arc. We found that the quality of sleep is dependent
on certain factors in socio-demographic characteristics. The state of health is dependent on
some factors in socio-demographic characteristics and living habits. We can also deduce that the
quality of sleep has an impact on the state of health. |
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