Self-adaptive Bayesian Fuzzy Inference Nets to Diagnose Cardiovascular Diseases

Boomadevi Sekar, Ming Chui Dong

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A generalized Bayesian inference nets model (GBINM) to aid developers to construct self-adaptive Bayesian inference nets for various applications and a new approach of defining and assigning statistical parameters to Bayesian inference nodes needed to calculate propagation of probabilities and address uncertainties are proposed. GBINM and the proposed approach are applied to design an intelligent medical system to diagnose cardiovascular diseases. Thousands of site-sampled clinical data are used for designing and testing such a constructed system. The preliminary diagnostic results show that the proposed methodology has salient validity and effectiveness.
LanguageEnglish
Pages181-190
Number of pages10
JournalInternational Journal of Knowledge-Based and Intelligent Engineering Systems
Volume18
Issue number3
DOIs
Publication statusPublished - Nov 2014

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Fuzzy inference
Testing
Uncertainty

Cite this

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abstract = "A generalized Bayesian inference nets model (GBINM) to aid developers to construct self-adaptive Bayesian inference nets for various applications and a new approach of defining and assigning statistical parameters to Bayesian inference nodes needed to calculate propagation of probabilities and address uncertainties are proposed. GBINM and the proposed approach are applied to design an intelligent medical system to diagnose cardiovascular diseases. Thousands of site-sampled clinical data are used for designing and testing such a constructed system. The preliminary diagnostic results show that the proposed methodology has salient validity and effectiveness.",
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Self-adaptive Bayesian Fuzzy Inference Nets to Diagnose Cardiovascular Diseases. / Sekar, Boomadevi; Dong, Ming Chui.

Vol. 18, No. 3, 11.2014, p. 181-190.

Research output: Contribution to journalArticle

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