Publications:Monitoring Human Larynx by Random Forests Using Questionnaire Data


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Title Monitoring Human Larynx by Random Forests Using Questionnaire Data
Author Antanas Verikas and Marija Bacauskiene and Adas Gelzinis and Virgilijus Uloza
Year 2011
PublicationType Conference Paper
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
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Abstract 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.