Comparative study of interval Type-2 and general Type-2 fuzzy systems in medical diagnosis

Ontiveros, Emanuel; Melín, Patricia; Castillo, Oscar

doi:10.1016/j.ins.2020.03.059

Cited by 102 publications

(35 citation statements)

References 20 publications

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“…Programming is not required. New created medical models and DSS are being usually compared by the accuracy with the results of doctors, 19,20 while ontological DSS give either a diagnosis that corresponds to 100% specific clinical recommendations or a hypothesis with an explanation of what facts are not enough to confirm it to 100% accuracy. Moreover, this ontological approach implies continuous improvement of the correctness and accuracy of KBs (for the medical ontology of diagnosis, a software tool for evaluating and continuously improving the KBs is created on the IACPaaS platform).…”

Section: Discussionmentioning

confidence: 99%

“…16,17 To improve the accuracy of automatic diagnostics fuzzy sets of Type-2 are used (in particular, they have grade of membership that are themselves fuzzy). 19 The developed theory of fuzzy sets (fuzzy sets of Type-2 after traditional fuzzy sets) is used at the preliminary stage of the DSS. Using fuzzy Type-2 systems, progress has been made in processing the uncertainty of the influence of patients ' symptoms and factors on the diagnosis.…”

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confidence: 99%

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Ontology of anomalous processes diagnosis

Грибова

Шалфеева

2020

Int J Intell Syst

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The diagnosis is complex in domains where processes occur over time inside systems or objects, changing and affecting other processes and characteristics. A large number of knowledge-based systems have been developed to increase the efficiency of the diagnostic process. But the models of knowledge of such systems do not simultaneously take into account all types of temporal relationships: changes in the values of attributes over time, taking into account the time intervals that have passed from the moments when the process began to develop and external influences, and so forth. The article considers the universal ontology of knowledge about the diagnosis of processes in various domains as a generalization and the result of many years of experience of the authors in the creation of intelligent systems for the diagnosis of processes. The ontology allows us to formalize anomalies as developing internal processes that are not inherent in a normally functioning system. On its basis (in its terms) knowledge bases and software components for decision support systems are constructed. In the case of a need for a domain-oriented ontology, it will only be necessary to redefine some concepts and relations which are features of specific domains.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Ontology of anomalous processes diagnosis

Грибова

Шалфеева

2020

Int J Intell Syst

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“…x are determined by using Eqs. (8) and (9) in [57]. Finally, based on the obtained final preference index, we utilize Eq.…”

Section:  mentioning

confidence: 99%

“…Thus, the important and necessary stages are consistency adjustment and alternatives' weights determination. To overcome these limitations, this paper focuses on introducing a new judgement matrix with Type-2 Fuzzy Sets (T2FSs) [6][7][8], and then we propose a new consistency-improving algorithm.…”

Section: Introductionmentioning

confidence: 99%

Interval Type-2 Trapezoidal Fuzzy Decision- Making Method With Consistency-Improving Algorithm and DEA Model

et al. 2020

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Interval type-2 fuzzy sets (IT2FSs) are useful and valuable tool to describe the decision makers' qualitative evaluation information. This paper designs a novel interval type-2 trapezoidal fuzzy decision-making (IT2TFDM) method, in which the local consistency adjustment strategy (LCAS) and interval type-2 fuzzy data envelopment analysis (DEA) are presented. First, in order to sufficiently describe the uncertain evaluation information, the definition of IT2TrFPRs is introduced, which is followed by the presentation of order consistency and multiplicative consistency for IT2TrFPRs. Then, an approach is proposed to check whether an IT2TrFPR is multiplicative consistent, and we construct a convergent consistency-improving algorithm, wherein the LCAS is utilized to retain the preference evaluation information of decision makers as much as possible. Furthermore, in order to determine the interval type-2 trapezoidal fuzzy priority weight vector, we investigate an interval type-2 trapezoidal fuzzy DEA model. An IT2TFDM method is proposed to obtain the reliable ranking of the alternatives. Finally, a fog-haze influence factor selection problem is given to show the practicality of the proposed IT2TFDM method, and the comparative analysis is carried out to clarify its validity and merits.

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“…Generally, type-2 fuzzy numbers are enormously applied in decision-making, risk assessment problems as they reflect the human judgment [37][38][39] and various fuzzy systems. [40][41][42] However, these numbers do not reflect the reliability, which is an important part in decision-making and risk assessment problems. As such Z-number has potential application in those problems as it tries to resolve the issue of the reliability of information.…”

Section: Introductionmentioning

confidence: 99%

Ranking of Z‐numbers based on value and ambiguity at levels of decision making

Chutia

2020

Int J Intell Syst

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The concept of Z-number, is very new in the literature, proposed by Zadeh in 2011. The Z-number Z A B = (,) is a pair of fuzzy numbers where the first component represents the restriction and the second component represents the certainty of the first component. Further, its application is evident in decision-making problems, risk assessment, linear programming problems, and so forth. Hence, under such circumstances ranking of Z-numbers need an utmost attention, as such, a few methods of ranking Z-numbers are proposed by various researcher. However, in many situations the existing methods depict drawbacks and limitations as discussed in this paper. Hence, a new method of ranking Z-numbers is essential for an appropriate decision-making. In this paper, a new method of ranking Z-numbers based on the concept of value and ambiguity at levels of decision-making have been proposed. The method seems to deliver a reasonable decision and proper ranking of Z-numbers of various types. A few numerical examples are discussed which show the out-performance of the proposed method.

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Comparative study of interval Type-2 and general Type-2 fuzzy systems in medical diagnosis

Cited by 102 publications

References 20 publications

Ontology of anomalous processes diagnosis

Ontology of anomalous processes diagnosis

Interval Type-2 Trapezoidal Fuzzy Decision- Making Method With Consistency-Improving Algorithm and DEA Model

Ranking of Z‐numbers based on value and ambiguity at levels of decision making

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