Abstract
Dyslexia is a condition where a person will face difficulties in certain tasks including reading, writing, speaking, and identifying sounds. Around 10% of people globally struggle with this issue. The most important step in preventing dyslexia is early identification. There are several ways to estimate the risk of dyslexia, where we have developed a model which allows the user to specify their language vocabulary, memory, speed, visual discrimination, audio discrimination test results. The model will determine the user’s individual risk of dyslexia after receiving input from the user. The approach we used included data preparation, data preprocessing, model training, model testing, and model construction. Predicting Risk of Dyslexia-PLOS ONE dataset is used. Dyslexia can be identified using machine learning classification techniques like Decision Trees, Random Forests, and Support Vector Machines. When compared to individual classification strategies, the ensemble technique in the proposed work predicts the risk of dyslexia with a better degree of accuracy. Here, we consider integrating GridSearch CV, Support Vector Machine, and Random Forest. Accuracy, precision, recall, and F1-score were taken into consideration as outcome measures.
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References
Protopapas A, Parrila R (5 April, 2018) Is dyslexia a brain disorder? Brain Sci 8(4):61. https://doi.org/10.3390/brainsci8040061. PMID: 29621138; PMCID: PMC5924397
Snowling MJ, Hulme C, Nation K (13 Aug, 2020) Defining and understanding dyslexia: past, present and future. Oxf Rev Educ. 46(4):501–513. https://doi.org/10.1080/03054985.2020.1765756. PMID: 32939103; PMCID: PMC7455053
Raschle NM, Chang M, Gaab N (1 Aug 2011) Structural brain alterations associated with dyslexia predate reading onset. Neuroimage 57(3):742–9. https://doi.org/10.1016/j.neuroimage.2010.09.055. Epub 2010 Sep 25. PMID: 20884362; PMCID: PMC3499031
Snowling MJ (1 Jan 2013) Early identification and interventions for dyslexia: a contemporary view. J Res Spec Educ Needs 13(1):7–14. https://doi.org/10.1111/j.1471-3802.2012.01262.x. PMID: 26290655; PMCID: PMC4538781
Werth R (2019) What causes dyslexia? Identifying the causes and effective compensatory therapy. Restor Neurol Neurosci 37(6):591–608. https://doi.org/10.3233/RNN-190939. PMID: 31796709; PMCID: PMC6971836
Nerušil B, Polec J, Škunda J, Kačur J (3 Aug 2021) Eye tracking based dyslexia detection using a holistic approach. Sci Rep 11(1):15687. https://doi.org/10.1038/s41598-021-95275-1. PMID: 34344972; PMCID: PMC8333039
Radford J, Richard G, Richard H, Serrurier M. Detecting dyslexia from audio records: an AI approach. https://doi.org/10.5220/0010196000580066
Brunswick N, Bargary S (28 Aug 2022) Self-concept, creativity and developmental dyslexia in university students: effects of age of assessment. Dyslexia 28(3):293–308. https://doi.org/10.1002/dys.1722. Epub 2022 Jul 11. PMID: 35818173; PMCID: PMC9543102
Chakraborty V, Sundaram M, Machine learning algorithms for prediction of dyslexia using eye movement. 06 Nov 2020 Bengaluru. https://doi.org/10.1088/1742-6596/1427/1/012012
Hassanain E. A multimedia big data retrieval framework to detect dyslexia among children. 2017 IEEE international conference on big data. 978-1-5386-2715-0/17
İleri R, Latifoğlu F, Demirci E (2020) New method to diagnosis of dyslexia using 1D-CNN, 2020 medical technologies congress (TIPTEKNO). Antalya, Turkey, pp 1–4. https://doi.org/10.1109/TIPTEKNO50054.2020.9299241
Seshadri NPG, Singh BK (2020) Hemispheric lateralization analysis in dyslexic and normal children using rest-EEG. 2020 IEEE recent advances in intelligent computational systems (RAICS). Thiruvananthapuram, India, pp 37–41. https://doi.org/10.1109/RAICS51191.2020.9332509
Frid A, Mane Vitz LM (2018) Features and machine learning for correlating and classifying between brain areas and dyslexia. arXiv:1812.10622
Ali J, Khan R, Ahmad N, Maqsood I (2012) Random forests and decision trees. Int J Comput Sci Issues (IJCSI) 9
Evgeniou T, Pontil M (2001) Support vector machines: theory and applications. 2049. 249–257. https://doi.org/10.1007/3-540-44673-7_12
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Saida, S.K., Snehitha, Y.Y., Priya, N.S., Babu, A.S.A. (2024). A Effective Method for Predicting the Dyslexia by Applying Ensemble Technique. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 785. Springer, Singapore. https://doi.org/10.1007/978-981-99-6544-1_12
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DOI: https://doi.org/10.1007/978-981-99-6544-1_12
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