Medical diagnosis and monotonicity clarification using SIRMs connected fuzzy inference model with functional weights

Author

Seki, Hiroshi ; Nakashima, Takayoshi

Author_Institution

Kwansei Gakuin Univ., Sanda, Japan

fYear

2014

fDate

6-11 July 2014

Firstpage

1662

Lastpage

1665

Abstract

This paper discusses the SIRMs (Single-Input Rule Modules) connected fuzzy inference model with functional weights (SIRMs model with FW). The SIRMs model with FW consists of a number of groups of simple fuzzy if-then rules with only a single attribute in the antecedent part. The final outputs of conventional SIRMs model are obtained by summarizing product of the functional weight and inference result from a rule module. In the SIRMs model of the paper, we firstly clarify its monotonicity. Secondly, we apply the SIRMs model with FW to medical diagnosis.

Keywords

fuzzy reasoning; fuzzy set theory; medical diagnostic computing; SIRM connected fuzzy inference model; functional weights; fuzzy if-then rules; medical diagnosis; monotonicity clarification; single-input rule modules; Computational modeling; Data models; Diabetes; Fuzzy logic; Inference algorithms; Medical diagnosis; Medical diagnostic imaging; Fuzzy inference; Single Input Rule Modules (SIRMs) connected fuzzy inference model; functional weight; medical data; monotonicity;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-2073-0

Type

conf

DOI

10.1109/FUZZ-IEEE.2014.6891866

Filename

6891866