Abstract
Learning Disorder refers to a number of disorder which may influence the understanding or use of verbal or nonverbal information. The most well-known types of learning disorder involve an issue with reading, writing, listening, and speaking. When we talk about learning disorder, most people only focusing on social development plan. Therefore, in this study, a systematic review was performed to identify, assess and aggregate on the prediction methods used for a predict learning disorder. The main objective of this paper is to, identify the most common prediction methods for learning disorder, in terms of accuracy by using the systematic review technique. From the main objective, we can define the research questions such as, which is the most common and the most accurate prediction methods used for learning disorder. In conclusion, the most common prediction methods for learning disorder which is Decision Tree and Support Vector Machine. For accuracy, Decision Tree, Linear Discriminant Analysis and K-Nearest Neighbor methods have the highest prediction accuracy for a learning disorder. From these findings, this paper can guide others to predict learning disorder by using the most common methods to get the best result in term of accuracy.
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References
Farzi, S., Kianian, S., Rastkhadive, I.: Diagnosis of attention deficit hyperactivity disorder using deep belief network based on greedy approach. In: 5th International Symposium on Computational and Business Intelligence (ISCBI), pp. 96–99, Dubai (2017)
Altay, O., Ulas, M.: Prediction of the autism spectrum disorder diagnosis with linear discriminant analysis classifier and K-nearest neighbor in children. In: 6th International Symposium on Digital Forensic and Security (ISDFS), pp. 1–4, Antalya (2018)
Chamseddine, A., Sawan, M.: Deep learning based method for output regularization of the seizure prediction classifier. In: 2018 IEEE Life Sciences Conference (LSC), pp. 118–121, Montreal, QC (2018)
Stefanidis, V., Anogianakis, G., Evangelou, A., Poulos, M.: Learning difficulties prediction using multichannel brain evoked potential data. In: Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI), pp. 268–272, Sliema (2015)
Liu, W., Yu, X., Raj, B., Yi, L., Zou, X., Li, M.: Efficient autism spectrum disorder prediction with eye movement: a machine learning framework. In: International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 649–655, Xi’an (2015)
Omar, K.S., Mondal, P., Khan, N.S., Rizvi, M.R.K., Islam, M.N.: A machine learning approach to predict autism spectrum disorder. In: International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6, Cox’sBazar, Bangladesh (2019)
Duan, H.: Learning to predict where the children with ASD look. In: 25th IEEE International Conference on Image Processing (ICIP), pp. 704–708, Athens (2018)
Sen, B., Borle, C.N., Greiner, R., Brown, M.R.G.: A general prediction model for the detection of ADHDand Autism using structural and functional MRI. PLOS ONE 13(4), e0194856 (2018)
Heinsfeld, A.S., Franco, A.R., Craddock, R.C., Buchweitz, A., Meneguzzi, F.: Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clin. 17, 16–23 (2018)
Pream Sudha, V., Vijaya, M.S.: Machine learning-based model for identification of syndromic autism spectrum disorder. In: Krishna, A.N., Srikantaiah, K.C., Naveena, C. (eds.) Integrated Intelligent Computing, Communication and Security. SCI, vol. 771, pp. 141–148. Springer, Singapore (2019). https://doi.org/10.1007/978-981-10-8797-4_16
Duda, M., Zhang, H., Li, H., Wall, D.P., Burmeister, M., Guan, Y.: Brain-specific functional relationship networks inform autism spectrum disorder gene prediction. Transl. Psychiatry 8(1), 56 (2018)
Usta, M.B., et al.: Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders. Psychiatry Clin. Psychopharmacol. 29, 320–325 (2018)
Yahata, N., et al.: A small number of abnormal brain connections predicts adult autism spectrum disorder. Nat. Commun. 7(1), 11254 (2016)
Zhou, Y., Yu, F., Duong, T.: Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning. PLoS ONE 9(6), e90405 (2014)
Sanders, E.A., Berninger, V.W., Abbott, R.D.: Sequential prediction of literacy achievement for specific learning disabilities contrasting in impaired levels of language in grades 4 to 9. J. Learn. Disabil. 51(2), 137–157 (2017)
Baten, E., Desoete, A.: Mathematical (Dis)abilities within the opportunity-propensity model: the choice of math test matters. Front. Psychol. 9 (2018)
Mowlem, F.D., Rosenqvist, M.A., Martin, J., Lichtenstein, P., Asherson, P., Larsson, H.: Sex differences in predicting ADHD clinical diagnosis and pharmacological treatment. Eur. Child Adolesc. Psychiatry 28(4), 481–489 (2018)
Velki, T., Vrdoljak, G.: Gender as moderator and age as mediator variables in prediction of school adjustment by self-evaluated symptoms of ADHD. Primenjena Psihologija 12(1), 65–83 (2019)
Jacobson, L.A., Schneider, H., Mahone, E.M.: Preschool inhibitory control predicts ADHD group status and inhibitory weakness in school. Arch. Clin. Neuropsychol. 33(8), 1006–1014 (2017)
Björk, A., Rönngren, Y., Selander, J., Vinberg, S., Hellzen, O., Olofsson, N.: Health, lifestyle habits, and physical fitness among adults with ADHD compared with a random sample of a Swedish general population. Soc. Health Vulnerability 9(1), 1553916 (2018)
Haas, S.M., Derefinko, K.J., Waschbusch, D.A.: The use of multi method impulsivity assessment in the prediction of ADHD, conduct problems, and callous-unemotional symptoms. Pers. Individ. Differ. 116, 289–295 (2017)
Wong, H.K., et al.: Personalized medication response prediction for attention-deficit hyperactivity disorder: learning in the model space vs. learning in the data space. Front. Physiol. 8, 199 (2017)
Walker, S.J., Langefeld, C.D., Zimmerman, K., Schwartz, M.Z., Krigsman, A.: A molecular biomarker for prediction of clinical outcome in children with ASD, constipation, and intestinal inflammation. Sci. Rep. 9(1), 5987 (2019)
Julie, M.D., Kannan, B.: Prediction of learning disabilities in school age children using decision tree. In: Meghanathan, N., Boumerdassi, S., Chaki, N., Nagamalai, D. (eds.) ASUC/NeCoM/VLSI/WeST/WiMoN -2010. CCIS, vol. 90, pp. 533–542. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14493-6_55
Laouris, Y., Aristodemou, E., Makris, P.: Prediction of learning abilities based on a cross-modal evaluation of non-verbal mental attributes using video-game-like interfaces. In: Esposito, A., VÃch, R. (eds.) Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions. LNCS (LNAI), vol. 5641, pp. 248–265. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03320-9_24
Rosenblum, S., Ben-Simhon, H.A., Meyer, S., Gal, E.: Predictors of handwriting performance among children with autism spectrum disorder. Res. Autism Spectr. Disord. 60, 16–24 (2019)
Chambon, V., Farrer, C., Pacherie, E., Jacquet, P.O., Leboyer, M., Zalla, T.: Reduced sensitivity to social priors during action prediction in adults with autism spectrum disorders. Cognition 160, 17–26 (2017)
Shui, A.M., Katz, T., Malow, B.A., Mazurek, M.O.: Predicting sleep problems in children with autism spectrum disorders. Res. Dev. Disabil. 83, 270–279 (2018)
Sembiring, S., Zarlis, M., Hartama, D., Ramliana, S., Wani, E.: Prediction of student academic performance by an application of data mining techniques. In: International Conference on Management and Artificial Intelligence IPEDR, vol. 6, pp. 110–114 (2011)
Kitchenham, B.: Procedures for performing systematic reviews, Technical report. Department of Computer Science, Keele University (2004)
Kitchenham, B., et al.: Systematic literature reviews in software engineering - a tertiary study. Inf. Softw. Technol. 52(8), 792–805 (2010)
Vapnik, V.: The support vector method of function estimation. In: Nonlinear Modeling, pp. 55–85. Springer, Boston (1998)
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This work was supported by the Ministry of Education under Skim Geran Penyelidikan Fundamental (FRGS) (grant number FRGS/1/2018/ICT04/UKM/02/8).
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Jamhar, M.A., Salwana, E., Zulkifli, Z., Nayan, N.M., Abdullah, N. (2019). Prediction of Learning Disorder: A-Systematic Review. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2019. Lecture Notes in Computer Science(), vol 11870. Springer, Cham. https://doi.org/10.1007/978-3-030-34032-2_38
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DOI: https://doi.org/10.1007/978-3-030-34032-2_38
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