View source for Publications:Monitoring Human Larynx by Random Forests Using Questionnaire Data

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{{PublicationSetupTemplate|Author=Antanas Verikas, Marija Bacauskiene, Adas Gelzinis, Virgilijus Uloza
|PID=461866
|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), Intelligenta system (IS-lab) (3941));Bacauskiene, Marija (Kaunas University of Technology);Gelzinis, Adas (Kaunas University of Technology);Uloza, Virgilijus (Kaunas University of Medicine)
|Title=Monitoring Human Larynx by Random Forests Using Questionnaire Data
|PublicationType=Conference Paper
|ContentType=Refereegranskat
|Language=eng
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|HostPublication=Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, ISDA, Cordoba, 22-24 november, 2011
|Conference=The 11th International Conference on Intelligent Systems Design and Applications
|StartPage=914
|EndPage=919
|Year=2011
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|Publisher=IEEE Computer Society
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|ISBN=978-1-4577-1676-8
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|DOI=http://dx.doi.org/10.1109/ISDA.2011.6121774
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|ScopusId=2-s2.0-84857544224
|NBN=urn:nbn:se:hh:diva-16647
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|Keywords=Random forests; Variable importance; Variable selection; Classifier; Data proximity; Human larynx
|Categories=Teknik och teknologier (2)
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|Abstract=<p>This paper is concerned with noninvasive monitoring of human larynx using subject’s questionnaire data. By applying random forests (RF), questionnaire data arecategorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important questionnaire statements are determined using RF variable importance evaluations. To explore multidimensional data, t-Distributed Stochastic Neighbor Embedding (t-SNE) and multidimensionalscaling (MDS) are applied to the RF data proximity matrix.When testing the developed tools on a set of data collectedfrom 109 subjects, 100% classification accuracy was obtainedon unseen data coming from two—healthy and pathological—classes. The accuracy of 80.7% was achieved when classifyingthe data into the healthy, cancerous, and noncancerous classes.The t-SNE and MDS mapping techniques facilitate data explorationaimed at identifying subjects belonging to a ”riskgroup”. It is expected that the developed tools will be of greathelp in preventive health care in laryngology.</p>
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|CreatedDate=2011-12-05
|PublicationDate=2011-12-05
|LastUpdated=2014-11-10
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:461866}}
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