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Using the patient's questionnaire data to screen laryngeal disorders
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0003-2185-8973
Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
Department of Otolaryngology, Kaunas University of Medicine, LT-50009 Kaunas, Lithuania.
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2009 (English)In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 39, no 2, p. 148-155Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2009. Vol. 39, no 2, p. 148-155
Keyword [en]
Larynx pathology, Variable selection, Genetic search, Support vector machine, Curvilinear component analysis, Query data
National Category
Otorhinolaryngology
Identifiers
URN: urn:nbn:se:hh:diva-101DOI: 10.1016/j.compbiomed.2008.11.008ISI: 000264030300006PubMedID: 19144329Scopus ID: 2-s2.0-59649090951OAI: oai:DiVA.org:hh-101DiVA, id: diva2:235792
Available from: 2009-09-18 Created: 2009-09-18 Last updated: 2017-12-13Bibliographically approved

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Verikas, Antanas

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