Evaluation of an automatic dry eye test using MCDM methods and rank correlation.
Dry eye is an increasingly common disease in modern society which affects a wide range of population and has a negative impact on their daily activities, such as working with computers or driving. It can be diagnosed through an automatic clinical test for tear film lipid layer classification based o...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 4; pp. 527 - 537 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121883623&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121883623 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2017 vid: 55 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 121883623 121883623 NLM27311605 10.1007/s11517-016-1534-5 NLM27311605 121883623 ppf: 527 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Evaluation of an automatic dry eye test using MCDM methods and rank correlation. aug: au: Peteiro-Barral, Diego Remeseiro, Beatriz Méndez, Rebeca Penedo, Manuel Méndez, Rebeca Penedo, Manuel G affil: Departamento de Computación , Universidade da Coruña , Campus de Elviña s/n 15071 A Coruña Spain sug: subj: Diagnosis, Computer Assisted Methods Dry Eye Syndromes Diagnosis Tears Information Science Methods Algorithms Lipids ab: Dry eye is an increasingly common disease in modern society which affects a wide range of population and has a negative impact on their daily activities, such as working with computers or driving. It can be diagnosed through an automatic clinical test for tear film lipid layer classification based on color and texture analysis. Up to now, researchers have mainly focused on the improvement of the image analysis step. However, there is still large room for improvement on the machine learning side. This paper presents a methodology to optimize this problem by means of class binarization, feature selection, and classification. The methodology can be used as a baseline in other classification problems to provide several solutions and evaluate their performance using a set of representative metrics and decision-making methods. When several decision-making methods are used, they may offer disagreeing rankings that will be solved by conflict handling in which rankings are merged into a single one. The experimental results prove the effectiveness of the proposed methodology in this domain. Also, its general purpose allows to adapt it to other classification problems in different fields such as medicine and biology. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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