This paper presents a fuzzy logic framework for dental caries and erosion risk assessment. Two interdependent modules are implemented within a cloud architecture. The first module is a fuzzy expert system designed for physicians and expert users, able to provide an active support in formulating risk judgements. The second module is oriented to generic users for oral health promotion. Conceptual ingredients of the fuzzy logic framework are principally defined by eliciting knowledge from a group of experts. The generation of rules involves both structured interviews and data driven learning procedures based on the use of neuro-fuzzy techniques.
This paper presents a fuzzy logic framework for dental caries and erosion risk assessment. Two interdependent modules are implemented within a cloud architecture. The first module is a fuzzy expert system designed for physicians and expert users, able to provide an active support in formulating risk judgements. The second module is oriented to generic users for oral health promotion. Conceptual ingredients of the fuzzy logic framework are principally defined by eliciting knowledge from a group of experts. The generation of rules involves both structured interviews and data driven learning procedures based on the use of neuro-fuzzy techniques.
A Cloud Fuzzy Logic Framework for Oral Disease Risk Assessment
Gonella G.;Binaghi E.;Vergani A.;BIOTTI, IRENE;Levrini L.
2019-01-01
Abstract
This paper presents a fuzzy logic framework for dental caries and erosion risk assessment. Two interdependent modules are implemented within a cloud architecture. The first module is a fuzzy expert system designed for physicians and expert users, able to provide an active support in formulating risk judgements. The second module is oriented to generic users for oral health promotion. Conceptual ingredients of the fuzzy logic framework are principally defined by eliciting knowledge from a group of experts. The generation of rules involves both structured interviews and data driven learning procedures based on the use of neuro-fuzzy techniques.
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