Difference between revisions of "Publications:Monitoring Human Larynx by Random Forests Using Questionnaire Data"
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| − | |Name=Verikas, Antanas | + | |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 | |Title=Monitoring Human Larynx by Random Forests Using Questionnaire Data | ||
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Latest revision as of 22:41, 30 September 2016
| 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 |
| Journal | |
| 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 |
| DOI | http://dx.doi.org/10.1109/ISDA.2011.6121774 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:461866 |
| 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. |
