Self-adaptive Bayesian Fuzzy Inference Nets to Diagnose Cardiovascular Diseases

Boomadevi Sekar, Ming Chui Dong

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Pages (from-to)181-190
Number of pages10
JournalInternational Journal of Knowledge-Based and Intelligent Engineering Systems
Issue number3
Publication statusPublished (in print/issue) - Nov 2014


Dive into the research topics of 'Self-adaptive Bayesian Fuzzy Inference Nets to Diagnose Cardiovascular Diseases'. Together they form a unique fingerprint.

Cite this