Fuzzy Bayesian Belief Network for Analyzing Medical Track Record

Intan, Rolly; Yuliana, Oviliani Yenty

doi:10.1007/978-3-642-12090-9_24

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…In a recent work of Intan and Yuliana (), fuzzy labels were assigned to some categories of a dataset, and a specific mechanism was developed in order to construct a BN for a data mining problem without using a fuzzy rule set as in our analysis (Lin et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Bayesian network construction using a fuzzy rule based approach for medical decision support

2014

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This study proposes a novel method for the construction of efficient and convenient Bayesian networks (BNs) and influence diagrams regarding medical problems based on fuzzy rules. The general methodology that was developed is able to address decisions based on fuzzy medical rules that connect symptoms with the severity/rating scale of a disease. These fuzzy rules are rich enough to cover a large variety of medical decisions. The method overcomes the major disadvantage of Bayesian nets, that is, the need of a vast amount of subjective probabilities. Instead of filling conditional probability tables in BNs, physicians report their knowledge in the form of fuzzy rules. The knowledge of these rules after defuzzification is transformed according to certain equations into probabilities. This becomes possible, categorizing the rules into certain types. For each type of rule, a mathematical expression is determined, which sets correctly the conditional probabilities that relate a symptom with its child severity. A particular example of assessing pulmonary infections and making decisions on severity degree was explored, implementing a decision support system (DSS) to show the functionality of the proposed methodology. The developed front-end DSS was evaluated and proved capable to drive fair decisions close to those obtained by physicians.

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Section: Introductionmentioning

confidence: 99%

Bayesian network construction using a fuzzy rule based approach for medical decision support

2014

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Methodologies for the Diagnosis of the Main Behavioral Syndromes for Parkinson’s Disease with Bayesian Belief Networks

Zaarour

Ali

Eddine

et al. 2015

Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology

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Fault Diagnosis of DCV and Heating Systems Based on Causal Relation in Fuzzy Bayesian Belief Networks Using Relation Direction Probabilities

2021

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The state-of-the-art provides data-driven and knowledge-driven diagnostic methods. Each category has its strengths and shortcomings. The knowledge-driven methods rely mainly on expert knowledge and resemble the diagnostic thinking of domain experts with a high capacity in the reasoning of uncertainties, diagnostics of different fault severities, and understandability. However, these methods involve higher and more time-consuming effort; they require a deep understanding of the causal relationships between faults and symptoms; and there is still a lack of automatic approaches to improving the efficiency. The data-driven methods rely on similarities and patterns, and they are very sensitive to changes of patterns and have more accuracy than the knowledge-driven methods, but they require massive data for training, cannot inform about the reason behind the result, and represent black boxes with low understandability. The research problem is thus the combination of knowledge-driven and data-driven diagnosis in DCV and heating systems, to benefit from both categories. The diagnostic method presented in this paper involves less effort for experts without requiring deep understanding of the causal relationships between faults and symptoms compared to existing knowledge-driven methods, while offering high understandability and high accuracy. The fault diagnosis uses a data-driven classifier in combination with knowledge-driven inference with both fuzzy logic and a Bayesian Belief Network (BBN). In offline mode, for each fault class, a Relation-Direction Probability (RDP) table is computed and stored in a fault library. In online mode, we determine the similarities between the actual RDP and the offline precomputed RDPs. The combination of BBN and fuzzy logic in our introduced method analyzes the dependencies of the signals using Mutual Information (MI) theory. The results show the performance of the combined classifier is comparable to the data-driven method while maintaining the strengths of the knowledge-driven methods.

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Fuzzy Bayesian Belief Network for Analyzing Medical Track Record

Cited by 3 publications

References 9 publications

Bayesian network construction using a fuzzy rule based approach for medical decision support

Bayesian network construction using a fuzzy rule based approach for medical decision support

Methodologies for the Diagnosis of the Main Behavioral Syndromes for Parkinson’s Disease with Bayesian Belief Networks

Fault Diagnosis of DCV and Heating Systems Based on Causal Relation in Fuzzy Bayesian Belief Networks Using Relation Direction Probabilities

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