Bell-shaped Fuzzy Soft Sets and Their Application in Medical Diagnosis

Dutta, Palash; Limboo, Bulendra

doi:10.1016/j.fiae.2017.03.004

Cited by 26 publications

(7 citation statements)

References 12 publications

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“…Now, let us consider that the parameters of the distribution of the life, PM and repair times are fuzzy numbers with bell shaped membership function (see [27] ) which is defined as follows

For arbitrary values for δ and ε , the intervals for the fuzzy parameters ϑ (assuming that

) are given as

where

01

and

is the value of the point estimator of ϑ .…”

Section: Model Descriptionmentioning

confidence: 99%

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Comparison of fuzzy semi-Markov models for one unit with mixed standby units with and without preventive maintenance using regenerative point method

Temraz

2021

Heliyon

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This paper introduced a study for a new system that consists of one unit with mixed standby units. The mathematical model for the system is constructed using semi-Markov model with regenerative point technique in two cases: the first case when there is preventive maintenance provided to the main unit and the second case when there is no preventive maintenance in the system. Life and repair times of the units in the system are assumed to be generally distributed with fuzzy parameters defined by the bell-shaped membership function. A Numerical application is introduced to compare the performance of the system in the two cases.

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“…Now, let us consider that the parameters of the distribution of the life, PM and repair times are fuzzy numbers with bell shaped membership function (see [27] ) which is defined as follows

For arbitrary values for δ and ε , the intervals for the fuzzy parameters ϑ (assuming that

) are given as

where

01

and

is the value of the point estimator of ϑ .…”

Section: Model Descriptionmentioning

confidence: 99%

“…Now, let us consider that the parameters of the distribution of the life, PM and repair times are fuzzy numbers with bell shaped membership function (see [27]) which is defined as follows…”

Section: The Parameters Of the Model As Fuzzy Numbersmentioning

confidence: 99%

Comparison of fuzzy semi-Markov models for one unit with mixed standby units with and without preventive maintenance using regenerative point method

Temraz

2021

Heliyon

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show abstract

“…For example, Farhadinia () refined the definition of weighted hesitant FS and corresponding correlation measures and then gave a practical example in medical diagnosis. Dutta and Limboo () gave the definition of bell‐shaped fuzzy soft set and introduced its application in medical diagnosis using arithmetic operations. Immaculate and Arockiarani () utilized neutrosophic rough sets to solve diagnosis problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

Yan

Liu

et al. 2019

Expert Systems

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Patients always want a precise diagnosis and appropriate treatment advice when they are diagnosed with a disease. Furthermore, the original information about certain features and symptoms appears in different forms, and the effect of time is always ignored in the process of diagnosis. To overcome these defects, this paper develops a systematic multiperiod hybrid decision support model, which combines the similarity measurement and three‐way decision theory to provide prediction and treatment advice for patients under the fuzzy environment. This multiperiod hybrid decision support model, which considers the effect of time, including transformation module, multiperiod integration module, similarity module, prediction module, and three‐way decision module, provides disease prediction and advice on treatment based on similarities and three‐way decision theory. To validate this model, we construct an illustration composed of four cases, and this ultimately shows that MPH‐SDM can effectively support patients' disease diagnoses and treatment.

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“…Divergence measure for intuitionistic fuzzy sets and its application in medical diagnosis was studied [44]. A study on Bell-shaped fuzzy soft sets and their applications in medical diagnosis were presented [45]. Medical diagnosis using distance measure on picture fuzzy set theory was discussed [46].…”

Section: Introductionmentioning

confidence: 99%

Medical Decision Making Using Arithmetic of Parabolic Fuzzy Numbers

Dutta

Doley

2020

SN COMPUT. SCI.

Self Cite

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Imprecision or uncertainty is an integral component of medical decision making. This paper attempts to bring out medical decision making using arithmetic of parabolic fuzzy numbers by preparing a list of potential symptoms of a patient. Furthermore, ranking of parabolic fuzzy number is defined. Finally, the present study seeks to find out the accurate problem of the patient through the listed symptoms and an effort has been made to address the issue via the arithmetic and ranking of PFNs.

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Bell-shaped Fuzzy Soft Sets and Their Application in Medical Diagnosis

Cited by 26 publications

References 12 publications

Comparison of fuzzy semi-Markov models for one unit with mixed standby units with and without preventive maintenance using regenerative point method

Comparison of fuzzy semi-Markov models for one unit with mixed standby units with and without preventive maintenance using regenerative point method

A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

Medical Decision Making Using Arithmetic of Parabolic Fuzzy Numbers

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