Induction of an Adaptive Neuro-Fuzzy Inference System for investing fluctuation in Parkinson’s disease

Document identifier: oai:dalea.du.se:1917
Keyword: ANFIS, Knowledge discovery, Parkinson, Fluctuation
Publication year: 2006
Relevant Sustainable Development Goals (SDGs):

The SDG label(s) above have been assigned by OSDG.ai

Abstract:

This paper presents a methodology to formulate natural language rules for an adaptive neuro-fuzzy system based on discovered knowledge, supported by prior knowledge and statistical modeling. Relationships between disease related variables and fluctuations in Parkinson’s disease is often complex. Experts have simplified but mostly reliable “fuzzy” rules based on experience. These rules could be improved using statistical methods and neural nets. This gives clinicians a valuable tool to explore the importance of different variables and their relations in a disease and could aid treatment selection. A prototype using the proposed methodology has been used to induce an Adaptive Neuro Fuzzy Inference Model that has been used to “discover” relationships between fluctuation, treatment and disease severity. More data is needed to confirm these findings. The project shows that artificial intelligence techniques and methods in combination with statistical methods offer medical research and applications valuable opportunities.

Authors

Shahina Begum

Other publications >>

Jerker Westin

Högskolan Dalarna; Datateknik
Other publications >>

Peter Funk

Other publications >>

Mark Dougherty

Högskolan Dalarna; Datateknik
Other publications >>

Record metadata

Click to view metadata

	
    header: 
        identifier: oai:dalea.du.se:1917
        datestamp: 2021-04-15T12:22:06Z
        setSpec: SwePub-du
    metadata: 
        mods: 
            @attributes: 
                version: 3.7
            recordInfo: 
                recordContentSource: du
                recordCreationDate: 2006-03-07
            identifier: http://urn.kb.se/resolve?urn=urn:nbn:se:du-1917
            titleInfo: 
                @attributes: 
                    lang: eng
                title: Induction of an Adaptive Neuro-Fuzzy Inference System for investing fluctuation in Parkinson’s disease
            abstract: 
This paper presents a methodology to formulate natural language rules for an adaptive neuro-fuzzy system based on discovered knowledge supported by prior knowledge and statistical modeling. Relationships between disease related variables and fluctuations in Parkinson’s disease is often complex. Experts have simplified but mostly reliable “fuzzy” rules based on experience. These rules could be improved using statistical methods and neural nets. This gives clinicians a valuable tool to explore the importance of different variables and their relations in a disease and could aid treatment selection. A prototype using the proposed methodology has been used to induce an Adaptive Neuro Fuzzy Inference Model that has been used to “discover” relationships between fluctuation treatment and disease severity. More data is needed to confirm these findings. The project shows that artificial intelligence techniques and methods in combination with statistical methods offer medical research and applications valuable opportunities.

            subject: 
                    @attributes: 
                        lang: eng
                    topic: ANFIS
                    @attributes: 
                        lang: eng
                    topic: knowledge discovery
                    @attributes: 
                        lang: eng
                    topic: Parkinson
                    @attributes: 
                        lang: eng
                    topic: fluctuation
            language: 
                languageTerm: eng
            genre: 
                conference/other
                ref
            note: 
                Published
                4
            name: 
                    @attributes: 
                        type: personal
                    namePart: 
                        Begum
                        Shahina
                    role: 
                        roleTerm: aut
                    @attributes: 
                        type: personal
                        authority: du
                    namePart: 
                        Westin
                        Jerker
                    role: 
                        roleTerm: aut
                    affiliation: 
                        Högskolan Dalarna
                        Datateknik
                    nameIdentifier: 
                        jwe
                        0000-0003-0403-338X
                    @attributes: 
                        type: personal
                    namePart: 
                        Funk
                        Peter
                    role: 
                        roleTerm: aut
                    @attributes: 
                        type: personal
                        authority: du
                    namePart: 
                        Dougherty
                        Mark
                    role: 
                        roleTerm: aut
                    affiliation: 
                        Högskolan Dalarna
                        Datateknik
                    nameIdentifier: mdo
            originInfo: 
                dateIssued: 2006
                place: 
                    placeTerm: Umeå
            relatedItem: 
                    @attributes: 
                        type: host
                    titleInfo: 
                        title: 23rd annual workshop of the Swedish Artificial Intelligence Society 
                    @attributes: 
                        type: series
                    titleInfo: 
                        title: Report / UMINF
                    identifier: 0348-0542
            physicalDescription: 
                form: print
            typeOfResource: text