A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment

Khatibi, Vahid; Montazer, Gholam Ali

doi:10.1016/j.eswa.2010.05.022

Cited by 75 publications

(28 citation statements)

References 18 publications

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“…Using Dempster-Shafer theory of evidence and fuzzy sets theory, Khatibi V. and Montazer G. A. [13] have proposed an inference engine named fuzzy-evidential hybrid inference engine. The hybrid engine functions in two phases.…”

Section: Related Workmentioning

confidence: 99%

Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules

Anooj

2011

Open Computer Science

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The development of medical domain applications has been one of the most active research areas recently. One example of a medical domain application is a detection system for heart disease based on computer-aided diagnosis methods, where the data is obtained from some other sources and is evaluated by computer based applications. Up to now, computers have usually been used to build knowledge based clinical decision support systems which used the knowledge from medical experts, and transferring this knowledge into computer algorithms was done manually. This process is time consuming and really depends on the medical expert's opinion, which may be subjective. To handle this problem, machine learning techniques have been developed to gain knowledge automatically from examples or raw data. Here, a weighted fuzzy rule-based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining the knowledge from the patient's clinical data. The proposed clinical decision support system for risk prediction of heart patients consists of two phases, (1) automated approach for generation of weighted fuzzy rules and decision tree rules, and, (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets obtained from the UCI repository and the performance of the system is compared with the neural network-based system utilizing accuracy, sensitivity and specificity.

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Section: Related Workmentioning

confidence: 99%

Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules

Anooj

2011

Open Computer Science

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“…This is different from the proposed screening models, namely, by performing modeling fuzzy rule-based and functionbased membership FRS. The concept is also used in research Khatibi and Montazer (2010), only in the study that combines two standard Framingham risk score and PROCAM, and uses five risk factors attribute. In the proposed research, with reference to the research conducted Versteylen et al (2011), Threshold of prediction of coronary heart disease (%) Sensitivity Specificity PPV NPV AUC Accuracy explains that standard FRS, able to provide better performance in comparison with another standard.…”

Section: Resultsmentioning

confidence: 99%

“…Similar research has also been conducted, which combines Framingham and PROCAM risk score for prediction of coronary heart disease. Merging the two standards using the method of fusion with Dempster-Shafer algorithm (Khatibi and Montazer, 2010). Based on previous studies, this study will propose an initial screening system model to predict the incidence of coronary heart disease based on fuzzy inference system.…”

Section: Introductionmentioning

confidence: 99%

Early screening of incidence coronary heart disease based on risk factor using fuzzy rule system

Wiharto

2017

Int. j. adv. appl. sci.

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A thorough examination in the context of the diagnosis of coronary heart disease requires a relatively high cost. To reduce the cost of the diagnosis can be done gradually, preceded by the initial screening based on risk factors. Initial screening can be done using a model that has been developed. Unfortunately the development of a model screening, reference to specific populations, such as the Framingham risk scores (FRS), so sometimes does not correspond to other populations. This study proposes a model of screening by combining FRS with artificial intelligence techniques, for initial screening on the risk of coronary heart disease. The research method is divided into several stages, firstly the selection of an attribute of risk factors. The second, modeling of the attribute into a fuzzy rule with reference to a standard FRS. The third, modeling of inference using Mamdani method, and the last of analyzing system performance. The test results show that the model proposed system has the ability, if positive patients tested, capable of producing a really positive output of coronary heart disease in the amount of sensitivity that is 91.37%. The performance is relatively better than a number of previous studies, with only requires an examination of the four attributes, namely age, sex, cholesterol and systolic blood pressure.

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“…The methods used in these papers are charged with two above mentioned restrictions of RIM ER approach. The method used in [16] is free of the second restriction while the first one is retained.…”

Section: Introductionmentioning

confidence: 99%

The Use of Intuitionistic Fuzzy Values in Rule-Base Evidential Reasoning

Dymova

Sevastjanov

Tkacz

2013

Artificial Intelligence and Soft Computing

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Abstract.A new approach to the rule-base evidential reasoning based on the synthesis of fuzzy logic, Atannasov's intuitionistic fuzzy sets theory and the Dempster-Shafer theory of evidence is proposed. It is shown that the use of intuitionistic fuzzy values and the classical operations on them directly may provide counter-intuitive results. Therefore, an interpretation of intuitionistic fuzzy values in the framework of DempsterShafer theory is proposed and used in the evidential reasoning. Using the real-world example, it is shown that such an approach provides reasonable and intuitively obvious results when the classical method of rule-base evidential reasoning cannot produce any reasonable results.

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A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment

Cited by 75 publications

References 18 publications

Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules

Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules

Early screening of incidence coronary heart disease based on risk factor using fuzzy rule system

The Use of Intuitionistic Fuzzy Values in Rule-Base Evidential Reasoning

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