Clinical Decision Support Model of Heart Disease Diagnosis Based on Bayesian Networks and Case-Based Reasoning
Man Xu and
Jiang Shen ()
Additional contact information
Man Xu: Nankai University
Jiang Shen: Nankai University
Chapter Chapter 23 in The 19th International Conference on Industrial Engineering and Engineering Management, 2013, pp 219-225 from Springer
Abstract:
Abstract To boost the accuracy of clinical decision support systems and degrade their misdiagnosis rates, a hybrid model was proposed with Bayesian networks (BN) and case-based reasoning (CBR). BN were constructed with the feature attributes and their casual relationships were learned. The similarities of feature attributes were measured with the case matching method, as well as the knowledge of their dependent relationships. Therefore, the accuracy of the diagnosis system was enriched through the dynamic retrieval method.
Keywords: BN; CBR; Heart disease diagnosis (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-38391-5_23
Ordering information: This item can be ordered from
http://www.springer.com/9783642383915
DOI: 10.1007/978-3-642-38391-5_23
Access Statistics for this chapter
More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().