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A Machine Learning Study of Comorbidity of Dyslexia and Attention Deficiency Hyperactivity Disorder

Abstract : Neurodevelopmental disorders in children like dyslexia and ADHD must be diagnosed at earlier stages as the children need to be provided with necessary aid. Comorbidity of dyslexia and ADHD is very high. Children with comorbidity of dyslexia and ADHD face comparatively more difficulty than children with just one of the disorders. Since all the three, dyslexia, ADHD and comorbid cases share many similar characteristics, it is hard to distinguish between cases which have only dyslexia or ADHD and those which have both. Manual analysis to differentiate based on standard scores of the psycho analysis tests provided inconsistent results. In this paper, we have applied standard machine learning techniques Random Forest, Support Vector Machine and Multilayer Perceptron to the diagnosis test results to classify between ADHD and comorbid cases, and dyslexia and comorbid cases. Analysis using the different individual psycho analysis tests is also done. Application of machine learning techniques provides better classification than the manual analysis.
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https://hal.inria.fr/hal-03434793
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Submitted on : Thursday, November 18, 2021 - 2:21:00 PM
Last modification on : Thursday, November 18, 2021 - 2:32:00 PM
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Junaita Davakumar, Arul Siromoney. A Machine Learning Study of Comorbidity of Dyslexia and Attention Deficiency Hyperactivity Disorder. 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.305-311, ⟨10.1007/978-3-030-63467-4_24⟩. ⟨hal-03434793⟩

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