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Using the patient's questionnaire data to screen laryngeal disorders
Abstract 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 categ...
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Published in: | Computers in biology and medicine 2009-02, Vol.39 (2), p.148-155 |
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creator | Verikas, A Gelzinis, A Bacauskiene, M Uloza, V Kaseta, M |
description | Abstract 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. |
doi_str_mv | 10.1016/j.compbiomed.2008.11.008 |
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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. 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diagnosis</topic><topic>Larynx pathology</topic><topic>MEDICIN</topic><topic>MEDICINE</topic><topic>Other</topic><topic>Otorhinolaryngologi</topic><topic>Otorhinolaryngology</topic><topic>Query data</topic><topic>Support vector machine</topic><topic>Surgery</topic><topic>Surveys and Questionnaires</topic><topic>Variable selection</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Verikas, A</creatorcontrib><creatorcontrib>Gelzinis, A</creatorcontrib><creatorcontrib>Bacauskiene, M</creatorcontrib><creatorcontrib>Uloza, V</creatorcontrib><creatorcontrib>Kaseta, M</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database (ProQuest)</collection><collection>Health & Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Database (1962 - 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Academic</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Högskolan i Halmstad</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Verikas, A</au><au>Gelzinis, A</au><au>Bacauskiene, M</au><au>Uloza, V</au><au>Kaseta, M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using the patient's questionnaire data to screen laryngeal disorders</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>2009-02-01</date><risdate>2009</risdate><volume>39</volume><issue>2</issue><spage>148</spage><epage>155</epage><pages>148-155</pages><issn>0010-4825</issn><issn>1879-0534</issn><eissn>1879-0534</eissn><coden>CBMDAW</coden><abstract>Abstract This paper is concerned with soft computing techniques for screening laryngeal disorders based on patient's questionnaire data. 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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.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>19144329</pmid><doi>10.1016/j.compbiomed.2008.11.008</doi><tpages>8</tpages></addata></record> |
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subjects | Automation Curvilinear component analysis Genetic search Humans Internal Medicine Kirurgi Laryngeal Diseases - diagnosis Larynx pathology MEDICIN MEDICINE Other Otorhinolaryngologi Otorhinolaryngology Query data Support vector machine Surgery Surveys and Questionnaires Variable selection |
title | Using the patient's questionnaire data to screen laryngeal disorders |
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