Rough Sets-Based Identification of Heart Valve Diseases Using Heart Sounds

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Rough Sets-Based Identification of Heart Valve Diseases Using Heart Sounds

Popis výsledku v původním jazyce

Recently, heart sound signals have been used in the detection of the heart valve status and the identification of the heart valve disease. Heart sound data sets represents real life data that contains continuous and a large number of features that couldbe hardly classified by most of classification techniques. Feature reduction techniques should be applied prior applying data classifier to increase the classification accuracy results. This paper introduces the ability of rough set methodology to successfully classify heart sound diseases without the need applying feature selection. The capabilities of rough set in discrimination, feature reduction classification have proved their superior in classification of objects with very excellent accuracy results. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including Support Vector Machine (SVM), Hidden Naive B

Název v anglickém jazyce

Rough Sets-Based Identification of Heart Valve Diseases Using Heart Sounds

Popis výsledku anglicky

Recently, heart sound signals have been used in the detection of the heart valve status and the identification of the heart valve disease. Heart sound data sets represents real life data that contains continuous and a large number of features that couldbe hardly classified by most of classification techniques. Feature reduction techniques should be applied prior applying data classifier to increase the classification accuracy results. This paper introduces the ability of rough set methodology to successfully classify heart sound diseases without the need applying feature selection. The capabilities of rough set in discrimination, feature reduction classification have proved their superior in classification of objects with very excellent accuracy results. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including Support Vector Machine (SVM), Hidden Naive B

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science. Volume 7208

ISBN

978-3-642-28941-5

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

10

Strana od-do

667-676

Název nakladatele

Springer Heidelberg

Místo vydání

Berlín

Místo konání akce

Salamanca

Datum konání akce

28. 3. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000309166900060