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Using k-dependence causal forest to mine the most significant dependency relationships among clinical variables for thyroid disease diagnosis
- Source :
- PLoS ONE, PLoS ONE, Vol 12, Iss 8, p e0182070 (2017)
- Publication Year :
- 2017
- Publisher :
- Public Library of Science (PLoS), 2017.
-
Abstract
- 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.
- Subjects :
- Pathology
Dependency (UML)
020205 medical informatics
Computer science
Peptide Hormones
lcsh:Medicine
Datasets as Topic
02 engineering and technology
computer.software_genre
Biochemistry
Trees
Bayes' theorem
Medicine and Health Sciences
0202 electrical engineering, electronic engineering, information engineering
Data Mining
Diagnosis, Computer-Assisted
lcsh:Science
Thyroid
Multidisciplinary
Plants
Markov Chains
Tree (data structure)
Physical Sciences
020201 artificial intelligence & image processing
Anatomy
Information Technology
Algorithms
Research Article
Computer and Information Sciences
Thyroid Hormones
medicine.medical_specialty
Markov Models
Neural Networks
Endocrine System
Machine learning
Markov model
Naive Bayes classifier
Diagnostic Medicine
medicine
Humans
Thyroid-Stimulating Hormone
Markov chain
business.industry
lcsh:R
Organisms
Biology and Life Sciences
Bayesian network
Bayes Theorem
Probability Theory
Thyroid Diseases
Hormones
Thyroxine
lcsh:Q
Artificial intelligence
business
computer
Mathematics
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....d3510d23e6e010616f9b24bec48f16d5