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

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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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  -