Optimal Design of a Fuzzy System with a Real-Coded Genetic Algorithm for Diabetes Classification

Monica, Julio Cesar; Melín, Patricia; Sánchez, Daniela

doi:10.1007/978-3-030-73050-5_32

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…This number is multiplied by 21 because each Type-3 Trapezoidal MF has seven parameters (p a1 , p b1 , p c1 , p d1 , p λ , p ↕1 , and p ↕2 ) and there are three Type-3 Trapezoidal MF in each fuzzy input. Three membership functions are used because previous works have shown that this number of membership functions allows good results for classification problems [30,31], as well as in other applications [47]. The three constants of the output are added to this multiplication, and finally, the multiplication of TR by 2 (consequents and activation status).…”

Section: Parameters Minimum Maximummentioning

confidence: 99%

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Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

Melin,

Sánchez,

Castillo

2023

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An essential aspect of healthcare is receiving an appropriate and opportune disease diagnosis. In recent years, there has been enormous progress in combining artificial intelligence to help professionals perform these tasks. The design of interval Type-3 fuzzy inference systems (IT3FIS) for medical classification is proposed in this work. This work proposed a genetic algorithm (GA) for the IT3FIS design where the fuzzy inputs correspond to attributes relational to a particular disease. This optimization allows us to find some main fuzzy inference systems (FIS) parameters, such as membership function (MF) parameters and the fuzzy if-then rules. As a comparison against the proposed method, the results achieved in this work are compared with Type-1 fuzzy inference systems (T1FIS), Interval Type-2 fuzzy inference systems (IT2FIS), and General Type-2 fuzzy inference systems (GT2FIS) using medical datasets such as Haberman’s Survival, Cryotherapy, Immunotherapy, PIMA Indian Diabetes, Indian Liver, and Breast Cancer Coimbra dataset, which achieved 75.30, 87.13, 82.04, 77.76, 71.86, and 71.06, respectively. Also, cross-validation tests were performed. Instances established as design sets are used to design the fuzzy inference systems, the optimization technique seeks to reduce the classification error using this set, and finally, the testing set allows the validation of the real performance of the FIS.

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Section: Parameters Minimum Maximummentioning

confidence: 99%

“…In Ref. [30], a real-coded GA is developed to design Type-1 FIS using five attributes of the dataset, where a comparison designing different fuzzy if-then rules was presented, demonstrating the importance of designing them. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

Melin,

Sánchez,

Castillo

2023

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“…Various fuzzy logic inference systems in combination with other techniques have been used. Type-1 fuzzy inference systems (FISs) were applied for diabetes classification, optimizing the parameters of three triangular membership functions (MF) for each variable using a genetic algorithm (GA) that has been proven effective as a search algorithm [5]. An optimization of parameters for type-2 trapezoidal MFs and Mamdani or Sugeno fuzzy models was compared with the performance of type-1 FIS that was optimized using the same type of MFs.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Fuzzy Inference System for Estimating Risk of Type 2 Diabetes

Tašić,

Nagy-Perjési,

Takács

2024

Mathematics

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In this paper, we present a multilevel fuzzy inference model for predicting the risk of type 2 diabetes. We have designed a system for predicting this risk by taking into account various factors such as physical, behavioral, and environmental parameters related to the investigated patient and thus facilitate experts to diagnose the risk of diabetes. The important risk parameters of type 2 diabetes are identified based on the literature and the recommendations of experts. The parameters are scaled and fuzzified on their own universe and, based on the experts’ recommendation, fuzzy inference subsystems are created with 3–4 related risk parameters to calculate the risk level. These sub-systems are then arranged into Mamdani-type inference systems so that the system calculates an aggregated risk level. The overview of the large number of diverse types of risk factors, which may be difficult for specialists and doctors, is facilitated by the proposed system.

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Optimal Design of a Fuzzy System with a Real-Coded Genetic Algorithm for Diabetes Classification

Cited by 2 publications

References 19 publications

Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

Multilevel Fuzzy Inference System for Estimating Risk of Type 2 Diabetes

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