A Constructive Model for Dyslexia and ADHD Prediction using Feature Learning and Boosting Approaches
Keywords:
Dyslexia, ADHD, neurological disorder, Ada-boosting, fisher-score, least absolute shrinkage and selection operatorAbstract
Dyslexia and Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurological disorder characterized by speech and reading impairments. The disorder is commonly identified in school-aged people, most commonly in males and causes poor performance with low self-esteem. Based on the review, it is noted that there are various machine learning approaches are used for the prediction process, and the validation is done with the available dataset. However, the prediction process is complex in this cause due to the lack of a standard dataset, biologically-interpretable biomarkers, classifiers, under-fitting, over-fitting issues and so on. To successfully implement a better CDSS, some preliminary process needs to be done to enhance the prediction rate. It includes: data acquisition, pre-processing, and data augmentation process. Here, the available online dataset for dyslexia is occupied from the UCI Machine Learning source. Some appropriate features of dyslexia are acquired using Least Absolute Shrinkage and Selection Operator (LASSO) and Fisher-score Relief Model. A hybrid AdaBoosting is developed by integrating the conventional classifiers with the bagging and boosting model. The bagging and boosting process is considered during the training process. In this case, the model is simulated using the MATLAB 2020a simulation environment, and the performances are assessed to demonstrate the model's importance. Metrics including accuracy, error rate, precision, recall, and F1-score are examined together with other statistical measurements. To further compare these findings with currently used methods, these results are supplied. This investigation demonstrates that the expected model achieves a better trade-off and a higher level of prediction accuracy.
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Altoè, G., Bertoldo, G., ZandonellaCallegher, C., Toffalini, E., Calcagnì, A., Finos, L., &Pastore, M. (2020) Enhancing statistical inference in psychological research via prospective and retrospective design analysis. Frontiers in Psychology, 10:2893.
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1-48
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E., &Munafò, M. R. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14, 365-376.
Cirino, P. T., Rashid, F. L., Sevcik, R. A., Lovett, M. W., Frijters, J. C., Wolf, M., & Morris, R. D. (2002). Psychometric stability of nationally normed and experimental decoding and related measures in children with reading disability. Journal of Learning Disabilities, 35(6), 526-539.
Dai, L., Zhang, C., & Liu, X. (2016). A special Chinese reading acceleration training paradigm: To enhance the reading fluency and comprehension of Chinese children with reading disabilities. Frontiers in Psychology, 7:1937.
Ebrahimi, L., Pouretemad, H., Khatibi, A., & Stein, J. (2019). Magnocellular based visual motion training improves reading in Persian. Scientific Reports, 9:1142
Giofrè, D., Cumming, G., Fresc, L., Boedker, I., &Tressoldi, P. (2017). The influence of journal submission guidelines on authors’ reporting of statistics and use of open research practices. PLOS ONE, 12(4), e0175583.
Franceschini, S., Bertoni, S., Gianesini, T., Gori, S., &Facoetti, A. (2017). A different vision of dyslexia: Local precedence on global perception. Scientific Reports, 7:17462.
Horowitz-Kraus, T., Vannest, J. J., Kadis, D., Cicchino, N., Wang, Y. Y., & Holland, S. K. (2014). Reading acceleration training changes brain circuitry in children with reading difficulties. Brain and Behavior, 4(6), 886-902.
Koen, B. J., Hawkins, J., Zhu, X., Jansen, B., Fan, W., & Johnson, S. (2018). The location and effects of visual hemisphere-specific stimulation on reading fluency in children with the characteristics of dyslexia. Journal of Learning Disabilities, 51(4), 399-415.
Lofti, S., Rostami, R., Shokoohi-Yekta, M., Ward, R. T., MotamedYeganeh, N., Mathew, A. S., & Lee, H. J. (2020). Effects of computerized cognitive training for children with dyslexia: An ERP study. Journal of Neurolinguistics, 55:100904
*Luniewska, M., Chyl, K., Debska, A., Kacprzak, A., &Plewko, J. (2018). Neither action nor phonological video games make dyslexic children read better. Scientific reports, 8:549.
Meng, X., Lin, O., Wang, F., Jiang, Y., & Song, Y. (2014). Reading performance is enhanced by visual texture discrimination training in Chinese-speaking children with developmental dyslexia. PlosONE, 9(9):e108274. H
Wang, L. C., Liu, D., &Xu, Z. (2019). Distinct effects of visual and auditory temporal processing training on reading and readingrelated abilities in Chinese children with dyslexia. Annals of Dyslexia, 69, 166-185.
Wang, L. C. (2017). Effects of phonological training on the reading and reading-related abilities of Hong Kong children with dyslexia. Frontiers in Psychology, 8:1904.
Szucs, D., & Ioannidis, J. P. A. (2017). Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature.Plos Biology, 15(3), e2000797.
Andersson, U. &Lyxell, B. Working memory defcit in children with mathematical difculties: A general or specifcdefcit? Journal of Experimental Child Psychology 96, 197–228 (2007).
Staikova, E., Gomes, H., Tartter, V., McCabe, A. &Halperin, J. Pragmatic defcits and social impairment in children with ADHD. Journal of Child Psychology and Psychiatry 54, 1275–1283 (2013).
Helland, W., Posserud, M., Helland, T., Heimann, M. &Lundervold, A. Lanugage Impairments in Children with AD(H)D and in Children with Reading Disorder. Journal of Attention Disorders 20, 581–589 (2016).
Melby-Lervåg, M. & Hulme, C. Is working memory training efective? A meta-analytic review.Developmental Psychology 49, 270–291 (2013).
Faraone SV, Asherson P, Banaschewski T, Biederman J, Buitelaar JK, Ramos-Quiroga JA, et al. Attention-deficit/hyperactivity disorder. Nat Rev Dis Prim. 2015;1:15020.
Chang Z, Lichtenstein P, Asherson PJ, Larsson H. Developmental twin study of attention problems high heritabilities throughout development. JAMA Psychiatry. 2013;70:311–8.
Pettersson E, Larsson H, Lichtenstein P. Common psychiatric disorders share the same genetic origin: a multivariate sibling study of the Swedish population. Mol Psychiatry. 2016;21: 717–21
Rydell M, Taylor MJ, Larsson H Genetic and environmental contributions to the association between ADHD and affective problems in early childhood-A Swedish population-based twin study. Am J Med Genet B Neuropsychiatr Genet. 2017;174: 538–46.
Faraone SV, Ghirardi L, Kuja-Halkola R, Lichtenstein P, Larsson H. The familial co-aggregation of attention-deficit/hyperactivity disorder and intellectual disability: a register-based family study. J Am Acad Child Adolesc Psychiatry. 2017;56:167–74 e1.
Brikell I, Ghirardi L, D’Onofrio BM, Dunn DW, Almqvist C, Dalsgaard S, et al. Familial liability to epilepsy and attentiondeficit/hyperactivity disorder: a nationwide cohort study. Biol Psychiatry. 2018;83:173–80
Zayats T, Athanasiu L, Sonderby I, Djurovic S, Westlye LT, Tamnes CK, et al. Genome-wide analysis of attention deficit hyperactivity disorder in Norway. PLoS ONE. 2015;10: e0122501.
Mooney MA, McWeeney SK, Faraone SV, Hinney A, Hebebrand J, Consortium I, et al. Pathway analysis in attention deficit hyperactivity disorder: an ensemble approach. Am J Med Genet B Neuropsychiatr Genet. 2016;171:815–26.
Akutagava-Martins GC, Salatino-Oliveira A, Genro JP, Contini V, Polanczyk G, Zeni C, et al. Glutamatergic copy number variants and their role in attention-deficit/hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet. 2014;165:502–9
Carrey NJ, MacMaster FP, Gaudet L, Schmidt MH. Striatal creatine and glutamate/ glutamine in attention-deficit/ hyperactivity disorder.J Child AdolescPsychopharmacol.2007; 17:11–7.
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