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,
FluctuationPublication year: 2006Relevant Sustainable Development Goals (SDGs):
The SDG label(s) above have been assigned by OSDG.aiAbstract: 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
>>
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header:
identifier: oai:dalea.du.se:1917
datestamp: 2021-04-15T12:22:06Z
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recordCreationDate: 2006-03-07
identifier: http://urn.kb.se/resolve?urn=urn:nbn:se:du-1917
titleInfo:
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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
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Begum
Shahina
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authority: du
namePart:
Westin
Jerker
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roleTerm: aut
affiliation:
Högskolan Dalarna
Datateknik
nameIdentifier:
jwe
0000-0003-0403-338X
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namePart:
Funk
Peter
role:
roleTerm: aut
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Dougherty
Mark
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affiliation:
Högskolan Dalarna
Datateknik
nameIdentifier: mdo
originInfo:
dateIssued: 2006
place:
placeTerm: Umeå
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titleInfo:
title: 23rd annual workshop of the Swedish Artificial Intelligence Society
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type: series
titleInfo:
title: Report / UMINF
identifier: 0348-0542
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form: print
typeOfResource: text