Automatic fuzzy classification system for metabolic types detection
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096785" target="_blank" >RIV/61989100:27240/15:86096785 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1007/978-3-319-21206-7_5" target="_blank" >http://dx.doi.org/10.1007/978-3-319-21206-7_5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-21206-7_5" target="_blank" >10.1007/978-3-319-21206-7_5</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Automatic fuzzy classification system for metabolic types detection
Popis výsledku v původním jazyce
Patients suffering from obesity have different demands for medical treatment regarding the causes of their metabolic disorders. To propose new medical solutions to weight reduction, it is desirable to group patients exhibiting similar characteristics. This contribution describes an automatic fuzzy classification system capable of dividing obese patients into groups of diverse metabolic types. Metabolic data were acquired through energometry tests and bioimpedance measurements. Methods considered in thispaper are particularly Principal Component Analysis used for data set's reduction and fuzzy clustering method dividing patients into groups called clusters. Newly tested patients are then classified into designed clusters. A set of statistical hypothesis testing methods is eventually applied to verify the performed classification. The designed classification system could be applied in hospitals to help the doctors with design of an individual treatment for obese patients' groups.
Název v anglickém jazyce
Automatic fuzzy classification system for metabolic types detection
Popis výsledku anglicky
Patients suffering from obesity have different demands for medical treatment regarding the causes of their metabolic disorders. To propose new medical solutions to weight reduction, it is desirable to group patients exhibiting similar characteristics. This contribution describes an automatic fuzzy classification system capable of dividing obese patients into groups of diverse metabolic types. Metabolic data were acquired through energometry tests and bioimpedance measurements. Methods considered in thispaper are particularly Principal Component Analysis used for data set's reduction and fuzzy clustering method dividing patients into groups called clusters. Newly tested patients are then classified into designed clusters. A set of statistical hypothesis testing methods is eventually applied to verify the performed classification. The designed classification system could be applied in hospitals to help the doctors with design of an individual treatment for obese patients' groups.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
JC - Počítačový hardware a software
OECD FORD obor
—
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2015
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Advances in intelligent systems and computing. Volume 370
ISBN
978-3-319-21205-0
ISSN
2194-5357
e-ISSN
—
Počet stran výsledku
10
Strana od-do
49-59
Název nakladatele
Springer
Místo vydání
Basel
Místo konání akce
Ostrava
Datum konání akce
29. 6. 2015
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
000365130300005