2022
DOI: 10.1002/ima.22710
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An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis

Abstract: Diabetes is one of the most common and hazardous diseases, which can affect almost every organ in the body. Diagnosis of diabetes requires determining all vital parameters related to the disease. However, the nature of the data from those parameters is very uncertain, affecting the process of disease diagnosis. This article proposes an intelligent fuzzy inference rule-based predictive diabetes diagnosis model (IFIR_PDDM), providing content recommendations to patients with diabetes. The suggested model employs … Show more

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Cited by 63 publications
(6 citation statements)
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“…For instance, this work considers three symptoms for determining the disease using two linguistic words and makes eight other combinations. while generating a crisp value by defuzzification to handle the risk level [21]. In the second phase of the prevention i.e Fuzzy Logic-based CHD Control System (FLCHDCS), were discussed with Fig 8 . in the following section.…”
Section: Proposed Methodologymentioning
confidence: 99%
“…For instance, this work considers three symptoms for determining the disease using two linguistic words and makes eight other combinations. while generating a crisp value by defuzzification to handle the risk level [21]. In the second phase of the prevention i.e Fuzzy Logic-based CHD Control System (FLCHDCS), were discussed with Fig 8 . in the following section.…”
Section: Proposed Methodologymentioning
confidence: 99%
“…The evaluation of a model using the theory of fuzzy sets is performed by polling an expert system and by developing a fuzzy rule “if… then….”. In this case, an expert is provided with sets of known values of input linguistic parameters suitable for diagnosis in some cases [ 22 , 23 ].…”
Section: Methodsmentioning
confidence: 99%
“…Assisting physicians in making medical diagnoses and reducing the likelihood of misdiagnosis is one of the main categories of the medical referral system. To reduce the risk of physicians prescribing incorrect drugs [ 15 ], based on machine-learning algorithms (neural networks, Bayesian networks) and data mining (clustering, classification) algorithms, a variety of decision-support systems or recommendation systems have been developed to assist physicians in obtaining better diagnostic results and to remind physicians of some easily ignored problems [ 16 ]. A diabetes-prediction and -diagnosis model (IFIR_PDDM) based on intelligent fuzzy inference rules was proposed to provide content recommendations for diabetes patients and predict the likelihood that current patients have diabetes [ 17 ].…”
Section: Related Workmentioning
confidence: 99%