Document Type : Original Research Paper


1 Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

2 Department of Electrical Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran


Background and Objective Learning disability is a neurological disorder. Simply, learning disabilities result from a person's misunderstanding of the brain. Children with learning disabilities are more accurate and intelligent than their peers, but they may have difficulty in reading, writing, pronouncing, concentrating, reasoning, recalling, or organizing information. Reading is the most basic and essential tool of education. Because by acquiring this skill, one will be able to acquire the necessary information in the affairs of life. The advancement of science in the present century is so rapid that reading is one of the most important tools for understanding today's world. One can learn the results of others' research and studies in a short period of time. Reading is a complex process that involves many different components.
Learning disability is very common in childhood. The most important disability is reading disorder which is related to reading skills.  Among the skills a student learns in school, reading is especially important. Meanwhile, there are students in higher grades whose reading progress is significantly lower than the standard level compared to their calendar age. This research represents a hybrid scoring model using genetic algorithm and fuzzy set theory to manage uncertainty in diagnosis of reading disability.
Methods: For this, fuzzy classification models were applied for diagnosis of the reading disability. In the fuzzy system, the knowledge was extracted from a group of experts who were teachers and specialists. In the proposed model, the knowledge of experts was automatically extracted using the learning process of the Genetic algorithm. A dataset of 260 girl students was collected from the Marefat High school in the Alborz province in the years of 1394 and 1395. The performance of the proposed model was investigated using the ROC curve analysis.
Findings: The results show efficiency of the fuzzy classification model was increased to 98.51% after the rule learning with the Genetic algorithm. The proposed fuzzy classifier models uncertainty in the knowledge of expert to improve students’ progress.
Conclusion: The results of this algorithm show that compared to several other methods, the fuzzy-genetic combination method performs better than other methods. The results of the performance characteristic curve also prove this. Comparing the efficiency of the system and its analysis using ROC shows that fuzzy classification system is able to identify reading disorders with high reliability. In the future, we can adjust the parameters of the membership functions and also use other meta-algorithms to improve the method. The prevalence of learning disabilities, especially reading in students, indicates the need to use strategies to reduce this disorder to prevent students' academic pathology. Another limitation of this study is the impossibility of examining the relationship between reading disorder and important variables such as parents’ education level and socio-economic status. It is suggested that these limitations be considered in future studies.


Main Subjects

©2019 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers. 

[1] Karimzadeh P, Shirazi S, Nilipour, R. Design and evaluation of diagnostic test of reading. Journal of Rehabilitation Science and Research. 2003; 6: 11-7.  Persian.
[2] Niazi M, Zare M. Study of learning disabilities in elementary school students in Isfahan, Proceedings of the Congress of Pediatrics and Pediatric Surgery, Tehran, Arman Publication; 1995. Persian.
 [3] Investigating the awareness of primary school administrators of learning disabilities in the provinces of the country [master’s thesis]. Tehran. Persian.
[4] Nasrabadi SM, Mandana S. Investigating knowledge of middle and upper secondary education managers about special learning disabilities and their relationship with students' academic achievement. Educational Articles. 2004; 13: 33-44. Persian.
[5] Comparison of managers' awareness of learning disabilities in three elementary, secondary and secondary levels [master’s thesis]. Tehran: Persian.
[6] Faryar F, Rakhshan A. learning disability, Tabriz: Saba Publishing; 2009. Persian.
[7] Allah Radi M. Evaluation and comparison of visual perception, memory and visual and auditory sequences and phonological awareness skills in dyslexic and normal second grade children in Tehran [master’s thesis]. Tehran; 2009. Persian
[11] Hosseini R, Mazinani M. Classification of Uncertainty Sources in Intelligent Medical Image Processing and Analysis systems. In Proc. of Internal Conference of Computer Engineering and Science, Mashhad; 2014. Persian
[12] Reitano CT. System & method for dyslexia detection by analyzing spoken & written words.Journal of the Acoustical Society of America. 2003; 9.
[15] Mico-Tormos P, Cuesta-Frau D, Novak D. EarlyDyslexia Detection Techniques by means of OculographicSignals. Paper presented at the 2nd European Medical & Biological Engineering Conference, Vienna, Austria; 2002.
[19] Chen FS, Su Y. Application of Decision Tree Algorithm to the Identification of Students with Learning Disabilities. Department of Industrial Education and Technology, Taiwan; 2010.
[20] Upadhyay A, Singh SK. Classification of Learning Disable Students Using Artificial Neural Network. Dept. of Information Technology, Thakur college of Science and Commerce, Bhubaneswa; 2010.
[27] Ishibuchi H, Nakashimam T, Murata T. A fuzzy classifier system that generates linguistic rules for pattern classification problems.  Fuzzy Logic, Neural Networks, and Evolutionary Computation. pp 35-54; 1995.
[28] Rezaiee F, Hosseini R, Mazinani M. Designing a Fuzzy Inference System for Diagnosing Reading Disorder in Middle-Level Students. Computer Engineering, Mashhad, Iran; 2016. Persian.