ArticlePloS one2017
Using k-dependence causal forest to mine the most significant dependency relationships among clinical variables for thyroid disease diagnosis.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Effectiveness of oseltamivir treatment on clinical failure in hospitalized patients with lower respiratory tract infection.BMC infectious diseases · 2021Trial
- A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19.Applied soft computing · 2021Article
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Authors and funding
5 authors.
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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.
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