NDLI: Monitoring human larynx by random forests using questionnaire data

Content Provider	IEEE Xplore Digital Library
Author	Verikas, A. Bacauskiene, M. Gelzinis, A. Uloza, V.
Copyright Year	2011
Description	Author affiliation: Department of Electrical & Control Equipment, Kaunas University of Technology, 51368, Kaunas, Lithuania (Verikas, A.; Bacauskiene, M.; Gelzinis, A.) \|\| Department of Otolaryngology, Lithuanian University of Health Sciences, Kaunas, Lithuania (Uloza, V.)
Abstract	This paper is concerned with 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 multidimensional data, t-Distributed Stochastic Neighbor Embedding (t-SNE) and 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, 100% classification accuracy was obtained on unseen data coming from two — healthy and pathological — classes. The accuracy of 80.7% was achieved when classifying the data into the healthy, cancerous, and noncancerous classes. The t-SNE and MDS mapping techniques 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.
Starting Page	914
Ending Page	919
File Size	246634
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121774
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data proximity Human larynx Classifier Random forests Radio frequency Pathology Accuracy Variable importance Data visualization Vegetation Distance measurement Intelligent systems Variable selection
Content Type	Text
Resource Type	Article

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