Proceedings of the 2016 2nd International Conference on Advances in Energy, Environment and Chemical Engineering (AEECE 2016)

Enhancing Dependence Representation of Clinical Variables based on Bayesian Networks to Assist in Thyroid Disease Diagnosis and Treatment

Authors
Fangyuan Cao, Limin Wang
Corresponding Author
Fangyuan Cao
Available Online July 2016.
DOI
10.2991/aeece-16.2016.65How to use a DOI?
Keywords
Bayesian network classifier; medical diagnosis; k-dependence causal forest; maximum-weighted spanning tree;
Abstract

In the past few decades, a large number of data mining algorithms have been proposed to assist in the practical medical diagnosis. This paper proposes a novel algorithm, k-dependence causal forest (KCF) based on Bayesian network. The experiments conducted on the thyroid disease data set suggest the KCF model always has lower 0-1 loss and contains more conditional mutual information, which means the KCF model is suitable to assist in thyroid disease diagnosis and treatment.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 2nd International Conference on Advances in Energy, Environment and Chemical Engineering (AEECE 2016)
Series
Advances in Engineering Research
Publication Date
July 2016
ISBN
978-94-6252-231-2
ISSN
2352-5401
DOI
10.2991/aeece-16.2016.65How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Fangyuan Cao
AU  - Limin Wang
PY  - 2016/07
DA  - 2016/07
TI  - Enhancing Dependence Representation of Clinical Variables based on Bayesian Networks to Assist in Thyroid Disease Diagnosis and Treatment
BT  - Proceedings of the 2016 2nd International Conference on Advances in Energy, Environment and Chemical Engineering (AEECE 2016)
PB  - Atlantis Press
SP  - 313
EP  - 316
SN  - 2352-5401
UR  - https://doi.org/10.2991/aeece-16.2016.65
DO  - 10.2991/aeece-16.2016.65
ID  - Cao2016/07
ER  -