Self-adaptive Bayesian fuzzy inference nets to diagnose cardiovascular diseases

Article type: Research Article

Authors: Sekar, Booma Devi^{; *} | Dong, Mingchui

Affiliations: Department of ECE, Faculty of Science and Technology, University of Macau, Macau, China

Correspondence: [*] Corresponding author: Booma Devi Sekar, Department of ECE, Faculty of Science and Technology, University of Macau, Macau S.A.R, China. Tel.: +86 83 974 518; E-mail: [email protected]

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

Keywords: Hybrid intelligent system, generalized Bayesian inference nets model, statistical parameters, diagnosis of cardiovascular disease, solving uncertainty

DOI: 10.3233/KES-140299

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 18, no. 3, pp. 181-190, 2014