Fuzzy Neural Networks to Detect Cardiovascular Diseases Hierarchically

Sekar, Booma Devi; Ming, Dong; Lei, Wai Kei

doi:10.1109/cit.2010.137

Cited by 10 publications

(2 citation statements)

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“…Furthermore, Table 5 shows the comparison results of intelligent CVD diagnosis systems using adapted versions of AI technologies, including neural networks (NN) [ 35 ], fuzzy neural networks (FNN) [ 36 ], and the proposed HBFIN. For performing a fair comparison, these methods were developed and validated by our research team with the same medical database used in this paper.…”

Section: Evaluation Of Constructed Bayesian Fuzzy Inference Netsmentioning

confidence: 99%

Function Formula Oriented Construction of Bayesian Inference Nets for Diagnosis of Cardiovascular Disease

Sekar

Dong

2014

BioMed Research International

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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 diagnosis through a staged decision process, the Bayesian inference nets (BIN) are adapted. New approaches to construct a hierarchical multistage BIN using defined function formulas and a method employing fuzzy logic (FL) technology to quantify inference nodes with dynamic values of statistical parameters are proposed. The suggested methodology is validated by constructing hierarchical Bayesian fuzzy inference nets (HBFIN) to diagnose various heart pathologies from the deduced HDPs. The preliminary diagnostic results show that the proposed methodology has salient validity and effectiveness in the diagnosis of cardiovascular disease.

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Section: Evaluation Of Constructed Bayesian Fuzzy Inference Netsmentioning

confidence: 99%

Function Formula Oriented Construction of Bayesian Inference Nets for Diagnosis of Cardiovascular Disease

Sekar

Dong

2014

BioMed Research International

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“…Furthermore, the same statistical summaries available on resting ECG machines are provided. For purpose of detecting cardiovascular diseases (CVDs) hierarchically via hemodynamic parameters (HDPs) derived from sphygmogram, a hierarchical fuzzy neural networks (HFNNs) scheme is proposed in Shi et al (2010), which provides a noninvasive way to detect CVDs. To deduce conclusion via HFNNs using HDPs as evidences, method of variance analysis is used to categorize and reduce the dimension of feature space.…”

Section: Literature Reviewmentioning

confidence: 99%

Cardio Vascular Detection with Neuro Computing and Genetic Algorithm

Peter¹,

Somasundram²

2014

RJASET

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For human the most fundamental requirement is having a healthy life, which is being difficult to maintain day to day as we are getting more progress in technological era. Among the possible reasons of unnatural death, heart disease based causes are showing very significant part. The diagnosis of heart diseases is a vital and intricate job. The recognition of heart disease from diverse features or signs is a multi-layered problem that is highly sensitive with respect diagnostic tests and establishing the relationship with multiple parameters is very difficult. In result decision is not free from false assumptions and is frequently accompanied by impulsive effects. This encourages developing a more reliable and cost effective knowledge based algorithmic approach to detect the heart disease. From engineering point of view, solution for detecting the presence of heart diseases is developed with the concept of artificial intelligence in data mining in this study. Feed forward architecture of neural network technology is taken as platform of computation to generate the intelligence in association with well established field of genetic algorithm (GA). A comparative performance has presented between both learning concepts with various different size of architecture.

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Extracting Fuzzy Rules from Hierarchical Heterogeneous Neural Networks for Cardiovascular Diseases Diagnosis

Cui

Dong

2013

Advanced Data Mining and Applications

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Fuzzy Neural Networks to Detect Cardiovascular Diseases Hierarchically

Cited by 10 publications

References 10 publications

Function Formula Oriented Construction of Bayesian Inference Nets for Diagnosis of Cardiovascular Disease

Function Formula Oriented Construction of Bayesian Inference Nets for Diagnosis of Cardiovascular Disease

Cardio Vascular Detection with Neuro Computing and Genetic Algorithm

Extracting Fuzzy Rules from Hierarchical Heterogeneous Neural Networks for Cardiovascular Diseases Diagnosis

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