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Random forests based monitoring of human larynx using questionnaire data
Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0003-2185-8973
Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
Department of Otolaryngology, Kaunas University of Medicine, Kaunas, Lithuania.
2012 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 39, no 5, 5506-5512 p.Article in journal (Refereed) Published
Abstract [en]

This paper is concerned with soft computing techniques-based noninvasive monitoring of human larynx using subject’s questionnaire data. By applying random forests (RF), questionnaire data are categorized 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 data represented by variables used by RF, the t-distributed stochastic neighbor embedding (t-SNE) and the multidimensional scaling (MDS) are applied to the RF data proximity matrix. When testing the developed tools on a set of data collected from 109 subjects, the 100% classification accuracy was obtained on unseen data in binary classification into the healthy and pathological classes. The accuracy of 80.7% was achieved when classifying the data into the healthy, cancerous, noncancerous classes. The t-SNE and MDS mapping techniques applied allow obtaining two-dimensional maps of data and facilitate data exploration aimed at identifying subjects belonging to a “risk group”. It is expected that the developed tools will be of great help in preventive health care in laryngology.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2012. Vol. 39, no 5, 5506-5512 p.
Keyword [en]
Random forests, Variable importance, Variable selection, Classifier, Data proximity, Human larynx
National Category
Computer Science
Identifiers
URN: urn:nbn:se:hh:diva-16645DOI: 10.1016/j.eswa.2011.11.070ISI: 000301155300087Scopus ID: 2-s2.0-84855868516OAI: oai:DiVA.org:hh-16645DiVA: diva2:461859
Available from: 2011-12-05 Created: 2011-12-05 Last updated: 2017-04-25Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
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Output format
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