Using k-dependence causal forest to mine the most significant dependency relationships among clinical variables for thyroid disease diagnosis

Numerous data mining models have been proposed to construct computer-aided medical expert systems. Bayesian network classifiers (BNCs) are more distinct and understandable than other models. To graphically describe the dependency relationships among clinical variables for thyroid disease diagnosis a...

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Bibliographic Details
Published in	PloS one Vol. 12; no. 8; p. e0182070
Main Authors	Wang, LiMin, Cao, FangYuan, Wang, ShuangCheng, Sun, MingHui, Dong, LiYan
Format	Journal Article
Language	English
Published	United States Public Library of Science 17.08.2017 Public Library of Science (PLoS)
Subjects	Algorithms Archives & records Artificial intelligence Bayes Theorem Bayesian analysis Biology and Life Sciences Classification Classifiers Computer and Information Sciences Data Mining Data processing Datasets Datasets as Topic Diabetes Diagnosis Diagnosis, Computer-Assisted - methods Education Expert systems Feasibility studies Forests Heart Humans International conferences Knowledge discovery Laboratories Markov Chains Mathematical models Medical diagnosis Medicine and Health Sciences Physical Sciences Software engineering Studies Thyroid Thyroid cancer Thyroid diseases Thyroid Diseases - diagnosis Trees Ultrasonic imaging
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ISSN	1932-6203 1932-6203
DOI	10.1371/journal.pone.0182070

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Summary:	Numerous data mining models have been proposed to construct computer-aided medical expert systems. Bayesian network classifiers (BNCs) are more distinct and understandable than other models. To graphically describe the dependency relationships among clinical variables for thyroid disease diagnosis and ensure the rationality of the diagnosis results, the proposed k-dependence causal forest (KCF) model generates a series of submodels in the framework of maximum spanning tree (MST) and demonstrates stronger dependence representation. Friedman test on 12 UCI datasets shows that KCF has classification accuracy advantage over the other state-of-the-art BNCs, such as Naive Bayes, tree augmented Naive Bayes, and k-dependence Bayesian classifier. Our extensive experimental comparison on 4 medical datasets also proves the feasibility and effectiveness of KCF in terms of sensitivity and specificity.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0182070