Function Formula Oriented Construction of Bayesian Inference Nets for Diagnosis of Cardiovascular Disease
作者： Booma Devi Sekar , Mingchui Dong
DOI: 10.1155/2014/376378
关键词: Machine learning 、 Statistical parameter 、 Computer science 、 Inference 、 SIGNAL (programming language) 、 Function (mathematics) 、 Bayes' theorem 、 Bayesian probability 、 Bayesian inference 、 Artificial intelligence 、 Fuzzy logic
摘要: An intelligent cardiovascular disease (CVD) diagnosis system using hemodynamic parameters (HDPs) derived from sphygmogram (SPG) signal is presented to support the emerging patient-centric healthcare models. To replicate clinical approach of through a staged decision process, Bayesian inference nets (BIN) are adapted. New approaches construct hierarchical multistage BIN defined function formulas and method employing fuzzy logic (FL) technology quantify nodes with dynamic values statistical proposed. The suggested methodology validated by constructing (HBFIN) diagnose various heart pathologies deduced HDPs. preliminary diagnostic results show that proposed has salient validity effectiveness in disease.
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