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Multivariate predictive model for dyslexia diagnosis

Multivariate predictive model for dyslexia diagnosis Dyslexia is a specific disorder of language development that mainly affects reading. Etiological researches have led to multiple hypotheses which induced various diagnosis methods and rehabilitation treatments so that many different tests are used by practitioners to identify dyslexia symptoms. Our purpose is to determine a subset of the most efficient ones by integrating them into a multivariate predictive model. A set of screening tasks that are the most commonly used and representative of the different cognitive aspects of dyslexia was proposed to 78 children from elementary school (mean age = 9 years ± 7 months) exempt from identified reading difficulties and to 35 dyslexic children attending a specialized consultation for dyslexia. We proposed a multi-step procedure: within each category, we first selected the most representative tasks using principal component analysis and then we implemented logistic regression models on the preselected variables. Spelling and reading tasks were considered separately. The model with the best predictive performance includes eight variables from four categories of tasks and classifies correctly 94% of the children. The sensitivity (91%) and the specificity (95%) are both high. Forty minutes are necessary to complete the test. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Dyslexia Springer Journals

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References (63)

Publisher
Springer Journals
Copyright
Copyright © 2010 by The International Dyslexia Association
Subject
Linguistics; Language and Literature; Psycholinguistics; Education, general; Neurology
ISSN
0736-9387
eISSN
1934-7243
DOI
10.1007/s11881-010-0038-5
pmid
20680527
Publisher site
See Article on Publisher Site

Abstract

Dyslexia is a specific disorder of language development that mainly affects reading. Etiological researches have led to multiple hypotheses which induced various diagnosis methods and rehabilitation treatments so that many different tests are used by practitioners to identify dyslexia symptoms. Our purpose is to determine a subset of the most efficient ones by integrating them into a multivariate predictive model. A set of screening tasks that are the most commonly used and representative of the different cognitive aspects of dyslexia was proposed to 78 children from elementary school (mean age = 9 years ± 7 months) exempt from identified reading difficulties and to 35 dyslexic children attending a specialized consultation for dyslexia. We proposed a multi-step procedure: within each category, we first selected the most representative tasks using principal component analysis and then we implemented logistic regression models on the preselected variables. Spelling and reading tasks were considered separately. The model with the best predictive performance includes eight variables from four categories of tasks and classifies correctly 94% of the children. The sensitivity (91%) and the specificity (95%) are both high. Forty minutes are necessary to complete the test.

Journal

Annals of DyslexiaSpringer Journals

Published: Aug 3, 2010

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