View source for Publications:Using the patient's questionnaire data to screen laryngeal disorders

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{{PublicationSetupTemplate|Author=Antanas Verikas, Adas Gelzinis, Marija Bacauskiene, Virgilijus Uloza, Marius Kaseta
|PID=235792
|Name=Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Gelzinis, Adas (Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania);Bacauskiene, Marija (Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania);Uloza, Virgilijus (Department of Otolaryngology, Kaunas University of Medicine, LT-50009 Kaunas, Lithuania);Kaseta, Marius (Department of Otolaryngology, Kaunas University of Medicine, LT-50009 Kaunas, Lithuania)
|Title=Using the patient's questionnaire data to screen laryngeal disorders
|PublicationType=Journal Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=Computers in Biology and Medicine
|JournalISSN=0010-4825
|Status=published
|Volume=39
|Issue=2
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|StartPage=148
|EndPage=155
|Year=2009
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|Publisher=Elsevier
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|DOI=http://dx.doi.org/10.1016/j.compbiomed.2008.11.008
|ISI=000264030300006
|PMID=19144329
|ScopusId=2-s2.0-59649090951
|NBN=urn:nbn:se:hh:diva-101
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|Keywords=Larynx pathology;Variable selection;Genetic search;Support vector machine;Curvilinear component analysis;Query data
|Categories=Oto-rino-laryngologi (30218)
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|Abstract=<p>This paper is concerned with soft computing techniques for screening laryngeal disorders based on patient's questionnaire data. By applying the genetic search, the most important questionnaire statements are determined and a support vector machine (SVM) classifier is designed for categorizing the questionnaire data into the healthy, nodular and diffuse classes. To explore the obtained automated decisions, the curvilinear component analysis (CCA) in the space of decisions as well as questionnaire statements is applied. When testing the developed tools on the set of data collected from 180 patients, the classification accuracy of 85.0% was obtained. Bearing in mind the subjective nature of the data, the obtained classification accuracy is rather encouraging. The CCA allows obtaining ordered two-dimensional maps of the data in various spaces and facilitates the exploration of automated decisions provided by the system and determination of relevant groups of patients for various comparisons.</p>
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|CreatedDate=2009-09-18
|PublicationDate=2009-09-18
|LastUpdated=2014-11-10
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:235792}}
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