عنوان مقاله [English]
Background and Aim: The aim of the present study was early and accurate diagnosis of preschoolers at risk for dyslexia through designing an intelligent diagnostic system.
Materials and Methods: The current research was a “research and development” type of investigation, in terms of its goal, and a descriptive research, assessment, and diagnostic type study, in terms of data collection method. The Neuro-cognitive program designed by Delavarian et al. was used for evaluation of the children. The efficacy, accuracy, validity, and reliability of this program were proven in many previous studies. Participants were selected following cluster random sampling method and their neuro-cognitive functions were saved for two years until the definite diagnosis of each individual was determined and then the data was applied in designing the diagnostic system. Multilayer perceptron and radial basis function artificial neural networks were applied in designing the system and they were compared according to their accuracy and sensitivity.
Results: The average accuracy of the system in early diagnosis of preschoolers at risk for dyslexia, designed by multilayer perceptron neural network, reached to 94.40% and the network’s sensitivity and specificity were obtained to be 90.27 and 95.28%, respectively.
Conclusion: According to the high validity and reliability of the neuro-cognitive program and the high accuracy and sensitivity of the designed decision support system, the mentioned system could be used in early detection of at risk preschoolers, prior to entering the elementary school.
10. Delavarian M, Nayebi E, Dibajnia P, Afrooz Gh.A, Gharibzadeh Sh, Towhidkhah F. Desiagning an accurate system for distinuishment of ADHD from oppisiotional Defiant Disorder wirh Artificial Neural Network. Medical Rehabilitatuion Journal 1394; 4 (2): 90-98 [In Persian]. ##
11. Dreyfus G. (). Neural networks: an overview. Neural networks methodology and applications (EBook): 497. ##
12. Manghirmalani P, Panthaky Z, Jain K. Learning disability diagnosis and classification-a soft computing approach. InInformation and Communication Technologies (WICT) 2011: 479-484. ##
13. Best JR, Miller PH, Naglieri JA. Relations between executive function and academic achievement from ages 5 to 17 in a large. representative national sample. Learning and individual differences 2011; 21(4):327-36. ##
14. Kershner JR. A Mini-Review: Toward a Comprehensive Theory of Dyslexia. Journal of Neurology and Neuroscience 2015. ##
15. . Best JR, Miller PH, Naglieri JA. Relations between executive function and academic achievement from ages 5 to 17 in a large. representative national sample. Learning and individual differences 2011; 21(4):327-36. ##
16. Casale A. Identifying Dyslexic Students: The need for computer-based dyslexia screening in higher education. Professor Colin Riordan Vice-Chancellor 2006:69. ##
17. Protopapas A, Skaloumbakas C, Bali P. Validation of unsupervised computer-based screening for reading disability in the Greek elementary Grades 3 and 4. Learning Disabilities: A Contemporary Journal 2008;6(1):45-69. ##
18. Georgiou GK, Papadopoulos TC, Zarouna E, Parrila R. Are auditory and visual processing deficits related to developmental dyslexia?. Dyslexia 2012; 18(2):110-29. ##
19. Mat NS, Shamsuddin SN, bt Husain R, Makhtar M, Isa WM, Mohamad FS. A Conceptual Framework for Designing a Computer-based Dyslexia Screening Test. InThe Third International Conference on Informatics & Applications 2014: 46-5. ##
20. Toki, E.I., Zakopoulou, V., Pange, J. Preschoolers’ Learning Disabilities Assessment: New Perspectives in Computerized Clinical Tools. Sino-US English Teaching 2014; 11(6):401-410. ##
21. Andrade OV, Andrade PE, Capellini SA. Collective screening tools for early identification of dyslexia. Frontiers in psychology 2014; 5. ##
22. Yazdani F, Akbarfahimi M, Mehraban AH, Jalaei S, Torabi-nami M. A computer-based selective visual attention test for first-grade school children: design, development and psychometric properties. Medical journal of the Islamic Republic of Iran 2015; 29: 184. ##
23. Jain K, Manghirmalani P, Dongardive J, Abraham S. Computational diagnosis of learning disability. International Journal of Recent Trends in Engineering 2009; 2(3):64-6. ##
24. Andrade OV, Andrade PE, Capellini SA. Collective screening tools for early identification of dyslexia. Frontiers in psychology2014; 5. ##
25. Wu TK, Meng YR, Huang SC. Application of Artificial Neural Network to the Identification of Students with Learning Disabilities. InIC-AI 2006: 162-168. ##
26. Manghirmalani P, More D, Jain K. A fuzzy approach to classify learning disability. International journal of advanced research in artificial intelligence 2012; 1(2). ##