Fuzzy expert system approach for coronary artery disease screening using clinical parameters

Pal, Dulal; Mandana, Kayapanda; Pal, Sarbajit; Sarkar, Debranjan; Chakraborty, Chandan

doi:10.1016/j.knosys.2012.06.013

Cited by 105 publications

(32 citation statements)

References 41 publications

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“…Our proposed system is then compared with three other systems that have been proposed before, they are: weighted rules FLS by P.K. Anooj [9], Rough Set FLS by Setiawan et al [10] and Decision Tree FLS by Pal et al [4]. Table 3 shows the comparative results of the four different systems.…”

Section: Resultsmentioning

confidence: 99%

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Interval type-2 fuzzy logic system for diagnosis coronary artery disease

Sajiah

Setiawan

Wahyunggoro

2016

CST

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Coronary artery disease (CAD) is a disease that has been the deadliest disease in Indonesia. The ratio of cardiologists over potential patients is not appropriate either. Intelligent system which can help doctors or patients for cheap and efficient diagnosing CAD is needed. M edical record data, acquisition of cardiologist knowledge and computing technology can be utilized for developing fuzzy logic based intelligent system. Type-1 fuzzy logic system (T1 FLS) has been widely used in various fields. T1 FS has limitation in representing and modelling uncertainty and minimize the impact. Whereas, type-2 fuzzy set (T2 FS) was also introduced as fuzzy set that can model uncertainty more sophisticated. T2 FLS does have a higher degree of freedom when modeling uncertainty but it is quite difficult to make the membership function. An interval T2 FS is a T2 FS in which the membership grade on third dimension is the same everywhere so it is simpler than T2 FS. This paper aims to clarify the better capability of IT2 FLS over T1 FLS on the development of CAD diagnosis system. Rules and membership function were formulated with the help of fuzzy c-means. This study illustrated the causes of CAD risk factors, fuzzification, type reduction and defuzzification. The resulted system was tested with percentage split method (50%-50%) to produce training data and testing data. This test is performed ten times with random seed to separate the data set. The resulted system generates an average of 73.78% accuracy, 71.94% sensitivity and 76.52% specificity.

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Section: Resultsmentioning

confidence: 99%

“…Plaque is made up of fat, cholesterol, calcium and other elements. This condition is called atherosclerosis [4].…”

Section: Introductionmentioning

confidence: 99%

Interval type-2 fuzzy logic system for diagnosis coronary artery disease

Sajiah

Setiawan

Wahyunggoro

2016

CST

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“…In the fuzzy set, if membership function is 1, the element fully belongs to the set; if it is 0, the element does not belong to the set; if it is between 0 and 1, the element partially belongs to the set. In the deterministic approach, the membership function is either 0 or 1 (Pal et al, 2012).…”

Section: Fuzzy Logic Modelmentioning

confidence: 99%

A fuzzy expert system for response determining diagnosis and management movement impairments syndrome

Khadivar

Dolatkhah

Amiri

2017

IJBIS

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Diagnosis is a very important aspect of medical care. Expert systems are developed to make the skills of specialists available for non-specialists. These systems simulate human thinking and performance and approach the performance of expert systems to the performance of a human expert. The purpose of this study is to develop a fuzzy expert system for diagnosis and treatment of musculoskeletal disorders in elbow and shoulder. A fuzzy Delphi method is used to gather data related to symptoms and treatments. By knowledge acquisition, an expert system is developed. Components of the proposed system consist of a knowledge base, fuzzy inference engine, working memory, user interface and knowledge acquisition utilities. The developed system is able to diagnose 18 disorders of the elbow and the shoulder. To compare systemic diagnosis and expert diagnosis, SPSS software is used for statistical analysis. Because 26 out of 30 patients had a systemic diagnosis similar to the expert diagnosis, it can be concluded that 86.7% of systemic diagnoses are similar to expert diagnosis. In the absence of experts, this intelligent application can provide reliable diagnosis and treatment. Application of intelligent and semi-intelligent systems such as expert systems can aid the users to make decisions.

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“…Cadosa [21] Diagnosis of breathing disordered in sleep MIMIC [22] Decision support system in ICU Diagnosis of heart valve disease [23] Diagnosis of heart valve diseases Pig-vet [24] Diagnosis of pig disease Diagnosis and treatment of leukemia [10] Diagnosis and recommended treatment for leukemia IX-DBC [3] Diagnosis of breast cancer Code [25] Coronary artery tests Proper nutrition diagnosis expert system [2] Diagnosis of proper nutrition for patients Another concept that often arises in connection with fuzzy systems is the concept of fuzzy variables. Value of a fuzzy variable is expressed by words, phrases and statements.…”

Section: Fuzzy Expert Systemmentioning

confidence: 99%

“…Fuzzification converts the real numbers to fuzzy sets, knowledge base includes laws which express the relationship between input and output fuzzy variables [30], inference mechanism takes input from user, selects the rules from the rule base [25], processes the input based on these rules and provides the output [30], defuzzification converts the fuzzy set to a real number based on the defuzzification process [31]. Another important concept of fuzzy expert system is knowledge acquisition which provides an efficient means of knowledge collection and storage throughout the world.…”

Section: Fuzzy Expert Systemmentioning

confidence: 99%

A fuzzy expert system for diagnosis and treatment of musculoskeletal disorders in wrist

Amiri

Khadivar

2017

Teh. vjesn.

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A FUZZY EXPERT SYSTEM FOR DIAGNOSIS AND TREATMENT OF MUSCULOSKELETAL DISORDERS IN WRIST Fatemeh Mohammadi Amiri, Ameneh KhadivarOriginal scientific paper Medical expert system is an application which can effectively contribute to decisions on diagnosis and treatment of diseases. A major part of the knowledge related to diagnosis of symptoms and treatment of illnesses is experimental. The problem is that how this knowledge can be extracted and made available to others.The purpose of this study is to design an expert system for diagnosis and treatment of musculoskeletal disorders in wrist in fuzzy environment.The knowledge is achieved from 15 elites in the field of musculoskeletal disorders. For data gathering related to symptoms of the disorders, a fuzzy Delphi method is used. A Delphi method is also used for gathering data related to treatments. Finally, a fuzzy expert system is developed for diagnosis and treatment of musculoskeletal disorders in wrist using MATLAB software. The designed system is able to diagnose seven disorders of the wrist. The input to the system is the magnitude of symptoms and the output is the scores given by the system to disorders; finally, the disorder with the highest score is displayed as the systemic diagnosis for which treatments are suggested.The systemic diagnosis is compared to elite diagnosis using statistical analysis conducted by SPSS software. Results show a significant correlation between two variables. By comparing the variables, it is found that 86,7 % of the systemic diagnoses were similar to elite diagnoses. In the absence of elites, diagnosis and treatment can be performed to a relatively reliable level. As results of current research, medical expert systems can be used as a scientific source by medical students. It can also help users as an auxiliary diagnostic system to diagnose and treat diseases. Keywords: fuzzy approach; knowledge acquisition; medical expert system; musculoskeletal disorders; treatment Fuzzy ekspertni sustav za dijagnozu i liječenje mišićnoskeletnih poremećaja u ručnom zglobuIzvorni znanstveni članak Medicinski ekspertni sustav je aplikacija koja može značajno pomoći pri odlučivanju o dijagnozi i liječenju bolesti. Veći dio znanja koje se odnosi na dijagnozu simptoma i liječenje bolesti je eksperimentalan. Problem je kako to znanje ekstrahirati i učiniti dostupnim drugima. Svrha ovoga rada je napraviti ekspertni sustav za dijagnozu i liječenje mišićno skeletnih poremećaja u ručnom zglobu u fuzzy okruženju. Iskustvo se steklo od 15 elitnih stručnjaka iz područja mišićnoskeletnih poremećaja. Za prikupljanje podataka o simptomima poremećaja primijenjena je fuzzy Delphi metoda. Ta se metoda primijenila i za dobivanje podataka o liječenju. Konačno, razvijen je fuzzy ekspertni sustav za dijagnosticiranje i liječenje mišićno skeletnih poremećaja u ručnom zglobu primjenom softvera MATLAB. Moguće je dijagnosticirati sedam poremećaja zgloba primjenom toga sustava. Ulaz u sustav je magnituda simptoma, a izlaz su rezultati koje je sustav pronašao za te porem...

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Fuzzy expert system approach for coronary artery disease screening using clinical parameters

Cited by 105 publications

References 41 publications

Interval type-2 fuzzy logic system for diagnosis coronary artery disease

Interval type-2 fuzzy logic system for diagnosis coronary artery disease

A fuzzy expert system for response determining diagnosis and management movement impairments syndrome

A fuzzy expert system for diagnosis and treatment of musculoskeletal disorders in wrist

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