Fuzzy expert system: An example in prostate cancer

Castanho, Maria José de Paula; Barros, Laécio Carvalho de; Yamakami, Akebo; Vendite, Laércio Luís

doi:10.1016/j.amc.2007.11.055

Cited by 19 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The linear profile is obtained by using Eqs. (11) and (12). The modifiable profiles are achieved by using Eqs.…”

Section: The Concept Of the Fuzzy Extension F Of A Classical Function Fmentioning

confidence: 99%

“…This is the case when there is a lack of information about the very correspondence between the variables, and/or when this information is affected by imprecision and uncertainty and/or http://dx.doi.org/10.1016/j.apm.2014. 11.030 0307-904X/Ó 2014 Elsevier Inc. All rights reserved. when one or more of the related variables are not quantitative but linguistic.…”

Section: Introductionmentioning

confidence: 95%

“…It has also been successfully used in medicine as a useful tool for the diagnosis of diseases [10][11][12][13]. Sets of fuzzy rules are particularly useful in the environmental field, where the correlations between the variables are very frequently inherently imprecise.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy extension of a classical function: An alternative to the knowledge base for modelling imprecise relations

Peche

Rodríguez

2015

Applied Mathematical Modelling

View full text Add to dashboard Cite

“…The linear profile is obtained by using Eqs. (11) and (12). The modifiable profiles are achieved by using Eqs.…”

Section: The Concept Of the Fuzzy Extension F Of A Classical Function Fmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

See 1 more Smart Citation

Fuzzy extension of a classical function: An alternative to the knowledge base for modelling imprecise relations

Peche

Rodríguez

2015

Applied Mathematical Modelling

View full text Add to dashboard Cite

“…Khanale & Ambilwade [16] presented a FIS that diagnose the thyroid diseases. De Paula Castanho [17] proposes a fuzzy system to predict pathological stage of prostate cancer. Banerjee et al, [18] apply the method based on fuzzy rules to diagnose patients with oral precancers where fuzzy rules through If-Then were utilised.…”

Section: Introductionmentioning

confidence: 99%

Identifying Risk Factors of Diabetes using Fuzzy Inference System

Abdullah

2017

IJ-AI

View full text Add to dashboard Cite

Identification of the real risk factors of diabetes is still very much inconclusive. In this paper, fuzzy rules based system was devised to identify risk factors of diabetes. The system consists of five input variables: Body Mass Index, age, blood pressure, Creatinine, and serum cholesterol and one output variable: level of risk. Three Gaussian membership functions for linguistic terms are defined for each input variable. The level of risk is defined using three triangular membership functions to represent output variable. Based on the information from patients’ clinical audit reports, the system was used to classify the level of risk of fifty patients that currently undergoing regular diagnosis for diabetes treatment. The system successfully classified the risk into three levels of Low, Medium and High where three main contributing factors toward developing diabetes were identified.<br /> The three risk factors are age, blood pressure and serum cholesterol.<br /> The multi-input system that characterised by IF-THEN fuzzy rules provide easily interpretable result for identifying predictors of diabetes. Research to establish reproducibility and validity of the findings are left for future works.

show abstract

“…En Chile, se optó inicialmente por cuarentenas dinámicas (intermitentes) a nivel comunal [10][11][12][13][14][15][16][17][18][19][20][21] . En un esfuerzo por estandarizar la toma de decisiones, el gobierno anunció el 20 de julio de 2020, la implementación de un sistema de indicadores para el confinamiento gradual llamado Plan Paso a Paso.…”

unclassified

Aplicación de sistemas fuzzy para la toma de decisiones del Plan Paso a Paso: análisis de los indicadores para la comuna de Valdivia

Sánchez,

Méndez,

Brun

et al. 2023

Rev. méd. Chile

View full text Add to dashboard Cite

Artificial intelligence fuzzy system for the design of confinements during quarantinesBackground: Different modalities of quarantines were one of the main measures implemented worldwide to avoid the spread of SARS-CoV2 virus. Aim: To analyze and compare retrospectively the implementation of the Stepto-Step plan devised by the Chilean Ministry of Health during the pandemic. To propose a decision-making path based on an artificial intelligence fuzzy system to determine confinements in specific territories. Material and Methods: The Step-to-Step Plan threshold values such hospital network capacity, epidemic spreading, testing and contact tracing capability were modeled using fuzzy numbers and fuzzy rule-based systems. Results: Ministry of Health's decision-making opportuneness were unrelated with the Step-to-Step Plan indicators for deconfinement. Such disagreements undermined epidemiological indicators. Conclusions: Using an artificial intelligence system could improve decision-making transparency, emergency governance, and risk communication to the population.

show abstract

Fuzzy expert system: An example in prostate cancer

Cited by 19 publications

References 8 publications

Fuzzy extension of a classical function: An alternative to the knowledge base for modelling imprecise relations

Fuzzy extension of a classical function: An alternative to the knowledge base for modelling imprecise relations

Identifying Risk Factors of Diabetes using Fuzzy Inference System

Aplicación de sistemas fuzzy para la toma de decisiones del Plan Paso a Paso: análisis de los indicadores para la comuna de Valdivia

Contact Info

Product

Resources

About