Intelligent decision support for real time health care monitoring system

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86097014" target="_blank" >RIV/61989100:27240/15:86097014 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/15:86097014

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-13572-4_15" target="_blank" >http://dx.doi.org/10.1007/978-3-319-13572-4_15</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-13572-4_15" target="_blank" >10.1007/978-3-319-13572-4_15</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Intelligent decision support for real time health care monitoring system

Popis výsledku v původním jazyce

In the health care monitoring, data mining is mainly used for classification and predicting the diseases. Various data mining techniques are available for classification and predicting diseases. This paper analyzes and evaluates various classification techniques for decision support system and for assisting an intelligent health monitoring system. The aim of this paper is to investigate the experimental results of the performance of different classification techniques for classifying the data from different wearable sensors used for monitoring different diseases. The Base Classifiers Proposed used in this work are IBk, Attribute Selected Classifier, Bagging, PART, J48, LMT, Random Forest and the Random Tree algorithm. Experiments are conducted on wearable sensors vital signs data set, which was simulated using a hospital environment. The main focus was to reduce the dimensionality of the attributes and perform different comparative analysis and evaluation using various evaluation metho

Název v anglickém jazyce

Intelligent decision support for real time health care monitoring system

Popis výsledku anglicky

In the health care monitoring, data mining is mainly used for classification and predicting the diseases. Various data mining techniques are available for classification and predicting diseases. This paper analyzes and evaluates various classification techniques for decision support system and for assisting an intelligent health monitoring system. The aim of this paper is to investigate the experimental results of the performance of different classification techniques for classifying the data from different wearable sensors used for monitoring different diseases. The Base Classifiers Proposed used in this work are IBk, Attribute Selected Classifier, Bagging, PART, J48, LMT, Random Forest and the Random Tree algorithm. Experiments are conducted on wearable sensors vital signs data set, which was simulated using a hospital environment. The main focus was to reduce the dimensionality of the attributes and perform different comparative analysis and evaluation using various evaluation metho

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 334

ISBN

978-3-319-13571-7

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

10

Strana od-do

183-192

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Addis Ababa

Datum konání akce

17. 11. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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