Fuzzy Rules Induction Medical Diagnosis
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F04%3A00096490" target="_blank" >RIV/68407700:21230/04:00096490 - isvavai.cz</a>
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Fuzzy Rules Induction Medical Diagnosis
Popis výsledku v původním jazyce
This document reports the activity of Daniele Peri during the MIRACLE fellowship at the CVUT, Prague June-August 2003. The research was focused on Fuzzy Rules Induction in the domain of medical diagnosis. Following the results of previous work, a prototype of a fuzzy rule learning infrastructure was implemented. The initial fuzzy rule method chosen was the recently introduced FURL (Fuzzy Rule Learner). The FURL method was tested over three applications: a synthetic dataset, a Multiple Sclerosis Diseasediagnosis dataset and a Arrhythmia Diagnosis datasets. The FURL method was then augmented with negated antecedent and tests were performed over the tree datasets. Finally, the infrastructure was enriched with a CFR (Conditional Firing Rules) controller and experimental results over the three dataset were collected.
Název v anglickém jazyce
Fuzzy Rules Induction Medical Diagnosis
Popis výsledku anglicky
This document reports the activity of Daniele Peri during the MIRACLE fellowship at the CVUT, Prague June-August 2003. The research was focused on Fuzzy Rules Induction in the domain of medical diagnosis. Following the results of previous work, a prototype of a fuzzy rule learning infrastructure was implemented. The initial fuzzy rule method chosen was the recently introduced FURL (Fuzzy Rule Learner). The FURL method was tested over three applications: a synthetic dataset, a Multiple Sclerosis Diseasediagnosis dataset and a Arrhythmia Diagnosis datasets. The FURL method was then augmented with negated antecedent and tests were performed over the tree datasets. Finally, the infrastructure was enriched with a CFR (Conditional Firing Rules) controller and experimental results over the three dataset were collected.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
JC - Počítačový hardware a software
OECD FORD obor
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Návaznosti výsledku
Projekt
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Návaznosti
Z - Vyzkumny zamer (s odkazem do CEZ)
Ostatní
Rok uplatnění
2004
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů