A fuzzy-based methodology for the analysis of diabetic neuropathy

doi:10.1016/S0165-0114(01)00122-1

Fuzzy Sets and Systems

Volume 129, Issue 2, 16 July 2002, Pages 203-228

https://doi.org/10.1016/S0165-0114(01)00122-1 Get rights and content

Abstract

A new model for the fuzzy-based analysis of diabetic neuropathy is illustrated, whose pathogenesis so far is not well known. The underlying algebraic structure is a commutative l-monoid, whose support is a set of classifications based on the concept of linguistic variable introduced by Zadeh. The analysis is carried out by means of patient's anagraphical and clinical data, e.g. age, sex, duration of the disease, insulinic needs, severity of diabetes, possible presence of complications. The results obtained by us are identical with medical diagnoses. Moreover, analyzing suitable relevance factors one gets reasonable information about the etiology of the disease, our results agree with most credited clinical hypotheses.

References (27)

L. Di Lascio et al.
A commutative L-monoid for classifications with fuzzy attributes
Int. J. Approximate Reasoning
(2001)
A. Gisolfi
An algebraic fuzzy structure for the approximate reasoning
Fuzzy Sets and Systems
(1992)
A. Gisolfi et al.
Classifying through a fuzzy algebraic structure
Fuzzy Sets and Systems
(1996)
A. Gisolfi et al.
A complete, flexible fuzzy-based approach to the classification problem
Int. J. Approximate Reasoning
(1995)
A. Gisolfi et al.
An algebraic approximation to the classification with fuzzy attributes
Int. J. Approximate Reasoning
(1993)
W. Pedrycz
Why triangular membership function?
Fuzzy Sets and Systems
(1994)
L.A. Zadeh
Fuzzy sets
Inform. Control
(1965)
K.-P. Adlassnig
Fuzzy set theory in medical diagnosis
IEEE Trans. System, Man, Cybernet.
(1986)
K.S. Collazos, J.M. Barreto, S.M. Nassar, Fuzzy analogical reasoning for medical diagnosis,...
G. Comi et al.
Neurophysiological study of the effect of combined kidney and pancreas transplantation on diabetic neuropathy: a 2-year follow-up evaluation
Diabetologia
(1991)

S. Dellepiane, G. Venturi, G. Vernazza, A fuzzy model for the processing and recognition of MR pathological images, in:...

L. Di Lascio et al.

A fuzzy-based approach to stereotype selection in hypermedia

User Modeling User-Adapted Interaction

(1999)

L. Di Lascio, E. Fischetti, A. Gisolfi, Fuzzy modeling and evaluation of an ITM user, Computacion and Systemas, 2002,...

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A fuzzy-based methodology for the analysis of diabetic neuropathy

Abstract

Int. J. Approximate Reasoning

Fuzzy Sets and Systems

Fuzzy Sets and Systems

Int. J. Approximate Reasoning

Int. J. Approximate Reasoning

Fuzzy Sets and Systems

Inform. Control

Fuzzy set theory in medical diagnosis

IEEE Trans. System, Man, Cybernet.

Neurophysiological study of the effect of combined kidney and pancreas transplantation on diabetic neuropathy: a 2-year follow-up evaluation

Diabetologia

A fuzzy-based approach to stereotype selection in hypermedia

User Modeling User-Adapted Interaction